# Predicting Mental Health of Best Human Capital for Sustainable Organization through Psychological and Personality Health Issues: Shift from Traditional to Novel Machine Learning-Supervised Technique Approach

Authors: Muhammad Anees Khan; Sadique Ahmad; Mohammed A. El-Affendi; Rija Zaka; Saima Mahmood; Muhammad Jehangir
Journal: BioMed Research International
 (2022)

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## Abstract

Researchers in the past discussed the psychological issue like stress, anxiety, depression, phobias on various forms, and cognitive issues (e.g., positive thinking) together with personality traits on traditional research methodologies. These psychological issues vary from one human to other human based on different personality traits. In this paper, we discussed both psychological issues together with personality traits for predicting the best human capital that is mentally healthy and strong. In this research, we replace the traditional methods of research used in the past for judging the mental health of the society, with the latest artificial intelligence techniques to predict these components for attaining the best human capital. In the past, researchers have point out major flaws in predicting psychological issue and addressing a right solution to the human resource working in organizations of the world. In order to give solution to these issues, we used five different psychological issues pertinent to human beings for accurate prediction of human resource personality that effect the overall performance of the employee. In this regard, a sample of 500 data has been collected to train and test on computer through python for selecting the best model that will outperform all the other models. We used supervised AI techniques like support vector machine linear, support vector machine radial basis function, decision tree model, logistic regression, and neural networks. Results proved that psychological issue data from employee of different organizations are better means for predicting the overall performance based on personality traits than using either of them alone. Overall, the novel traditional techniques predicted that sustainable organization is always subject to the control of psychological illness and polishing the personality traits of their human capital.


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## Body

## 1. Introduction

Based on the data of the World Health Organization, one person out of four people is affected by the term called health concerns which affect both his mental and physical performance towards any job. Thus, mental health is such a type of situation where a person or individual realizes his personal abilities that help in normalizing the stress in general conditions for improving the overall productivity that in one or other way contributing to community and surroundings. The literature on physicians’ mental health is expanding day by day. The main reason behind the literature growth is the general concerns about field-related professionals and especially their mental well-being. On the other hand, the healthcare employees have a lack of focus on their work. As a result, increased efforts have been made around the world to promote physicians’ mental health and wellness, dubbed “The Quadruple Aim.” “In the past, researchers worked on social, mental, and psychological issues; however, after the era of using statistical techniques, researchers entered into the era of machine learning and artificial intelligence which started in 1990s. It started from using neural networks [1] to using supervised artificial intelligence techniques in 2020 by [2] psychological issue and personality trait indicators [3]. In Pakistan, researchers have predicted psychological and personality trait issues by using traditional statistical techniques, but no work is being done using artificial intelligence techniques.


## 2. Literature Review

Humans are different from each other because personality is a combination of feelings, experience, thoughts, and emotions. It also specifies how one should interact with various individuals in various situations. Some people behave differently in the same situation because their personality is made up of multiple traits. Personality predicts our conduct and has a significant influence on our decision-making abilities. The variance in decision-making sense from person to person is simply due to personality [4]. It is difficult to predict human behavior as it varies from time to time and situation to situation, and personality is not fully exposed and understood. Every individual behavior differs from the other, like their decision because of the different personality they possess. The behavior of investors is the result of their attitudes, personalities, norms values, and other factors such as level of motivation [4]. All these factors become the cause of irrational decision-making. Previously, various studies have been conducted in order to examine the factors that influence individuals’ financial and investment decisions. Out of the many factors, personality type is also a factor that influences financial and investment decision. However, financial or investment decisions at the level of individuals need to be generated by the conscious understanding of the individual’s level of motivation, personality traits, and other psychological factors. The lack of understanding of these factors may lead to irrational financial and investment decisions. Along with this, the financial advisor also needs to understand the workings of these cognitive factors to reach a critically informed decision. The literature in the relevant field highlights the needs to focus on the proposition that psychological factors play an important role in the decision-making processes. However, different techniques have been used for predicting the personality issues and psychological issues as the following: discriminant analysis is a statistical technique that is used to recognize the relationship between dependent variables and one or more independent variables. The primary difference between regression analysis and multiple discriminant analysis is that in regression analysis, dependent variable is a continuous variable, while discriminant analysis deals with categorical dependent variable [5, 6]. Moreover, using logistic regression differs with linear regression analysis in the use of dependent variable as logistic regression deals in dichotomous dependent variable [7]. In traditional statistical techniques used for predicting psychological issues, logit models also had a prominent place. Hence, the overall discussion about the machine learning is the science and art of programming computers so that they can comprehend patterns in data and develop algorithms for future prediction of data [8]. Patterns in data are identified by software, and an algorithm is developed. After that, algorithm is used for prediction on the basis of previously identified pattern in data. Supervised technique is a subpart of artificial intelligence and is specifically under the category of machine learning. As its name indicates, machine is made to learn under supervision. It means that a computer algorithm is constructed on the basis of training by input data that is being labelled for a specific output. In this way, computer learns the underlying similarities in data and recognizes relationships among the input data that is being used to train the machine. This training data set is a foundation for algorithm development, so this training data set should be well balanced and contains all such readings that are true representative of that data. To test its usefulness, a test data set is fed into that algorithm that results in measuring its accuracy [9].


## 3. Psychological Health Issues

According to several studies, personality has a role in understanding investor behavior. Costa and McCrae provide the big five model of personality, which includes extraversion (extremely chatty, lively), neuroticism (depressed, gloomy), conscientiousness (very organized), openness to experience (very creative), and agreeableness (kind and generous). In the literature, the authors used a questionnaire based on personality which supports the five-factor model [10]. Those who are well organized and highly creative and easily think and understand ideas and information are good in managing personal financial matters and investment decisions. Openness to experience, one of the dimensions of the big five model, contains both creative and thoughtful expressions. Another important dimension of this model also puts the impact on the investment decision, i.e., conscientiousness in which great efforts for expertise and achievements are involved [10].


## 4. Personality Health Traits

We need five traits that will build a personality, which include extraversion, agreeableness, neuroticism, openness to experience, and conscientiousness. Talking about extraversion is a trait that signifies individual variances in a social meeting, confidence, and energy level. Individuals who are highly extraverted relish socializing with others, while expressing themselves in a group situation, they are easily comfortable and frequently show a positive attitude by expressing emotions such as eagerness and excitement [11]. Along with the previous trait, agreeableness is a trait of personality that is based on the concept where variances in empathy, respectfulness, and acceptance of others are captured by agreeableness. Agreeable persons are highly sensitive towards other’s happiness, treat others very well for their personal privileges and preferences, and always think positive about others. Displeasing individuals tend to have less respect for others and social standards of graciousness [11]. Moreover, conscientiousness also signifies variances in organization, productiveness and accountability, command, and structure followed by a highly conscientious person. They work determinedly to achieve their objectives, and they are highly committed while fulfilling their responsibility and compulsion, whereas unconscientious persons are easy to go with the disorder and are less motivated to complete tasks [11]. In addition, it is also observed that Neuroticism is also changes due to intensity of negative emotions. Highly neurotic persons are more inclined to anxiety, sorrow, and mood swings, and they are emotionally unstable [11]. And the last one is openness to experience where variances in intellectual curiosity, artistic sensitivity, and imagination are indicated by openness to experience. Highly open individuals are more creative and enjoy thinking and learning; they are art and beauty conscious and create innovative ideas. Close-minded people tend to have a narrow series of rational and creative interests [11].


## 5. Computational Learning Theory

The purpose of computational learning theory is to comprehend the machine learning algorithm and decide what is learnable. It assists in determining how much data is required for the training of a specific algorithm as well as the required resources for the learning activity. According to the Association for Computational Learning, computational learning theory is “a research subject devoted to investigating the design and analysis of machine learning algorithms. Such algorithms strive to make accurate predictions or representations based on observations.” Computational learning theory is an area of theoretical computing that discusses the design of computer programs and their ability to learn, as well as the identification of computing limitations with machines. Computational learning theory aids in posing and answering concerns about the performance of learning algorithms.


## 6. Hypothesis of the Study

### 6.1. Predicting Psychological and Personality Health Issues through Neural Networks

In order to find the psychological issues and personality traits of successful human being for improving the overall performance, a study was conducted in the USA in which they used psychological issues and personality traits as independent variables and performance as the dependent variable. It was found that psychological issues (stress, anxiety, depression, phobias on various forms, and cognitive issues) and personality traits (conscientiousness, openness to experience, and extroverts) are highly skilled, or we can say that they are overconfident in their skills. On the other hand, neuroticism and openness to experience look towards more returns. Openness to experience and neuroticism were found more in successful investors. A neural network is a series of algorithms that aspires to acknowledge underlying relationships in a data set through a procedure that imitates the way human brain operates. Researchers in the past used neural network technique for psychological issue prediction [12]. In this regard, neural network approach has been introduced to discriminant analysis for predicting human psychological issue that leads to organizational performance. Researchers compared the results with linear classifier, logistic regression, KNN, and ID3 and found it to be an accurate method of measurement. Moreover, according to [13], results of psychological issue model constructed by neural networks outperformed model constructed by multiple discriminant analysis. Keeping the above discussion, the authors will suppose the following hypothesis for testing and contribution.Hypothesis 1a.
Predicting psychological issues through neural network algorithm outperforms all the other technique used in this study.Hypothesis 1b.
Predicting personality traits through neural network algorithm outperforms all the other technique used in this study.


### 6.2. Predicting Psychological Issues and Personality Traits through Decision Tree Model

The literature is saturated with many findings that discover statistical association among psychiatric difficulties, big 5 personality traits, and risk-taking attitude in preadolescents. Using multiple regression using questionnaires as data, it was discovered that extroverts and openness to experience are strongly associated with risk-taking attitudes, whereas conscientiousness, neuroticism, and agreeableness cannot predict risk-taking attitudes. Thus, this study found that a higher level of extroverts and openness to experience, as well as a lower level of conscientiousness, is associated with a higher level of risk-taking attitude. As discussed in previous lines with a traditional methods, researcher’s also used the decision tree model. So here, the decision tree model is a type of arithmetical calculation in which algorithms are build and those algorithms are said to be decision tree models in which inquiries and examinations are done in a calculative manner so that they may aid in holding subsequent tests. [14] used decision tree model for psychological test and organization risk measurement and got 91.2% accuracy result. Similarly, [15] also trains and tests the personality traits and psychological issue data to predict psychological issue indicators using random forest decision tree and obtains 90% average prediction accuracy. Thus, keeping the above discussion as a benchmark, the research develops and assumed the following hypothesis to work on.Hypothesis 2a.
Predicting psychological issues through decision tree algorithm outperforms all the other technique used in this study.Hypothesis 2b.
Predicting personality traits through decision tree algorithm outperforms all the other technique used in this study.


### 6.3. Predicting Psychological Issues and Personality Traits through Genetic Algorithms

In the past, traditional methodologies produced a large body of literature on the prediction of psychiatric problems and personality traits. For instance, by using multiple regression, it was discovered that personality trait influences financial risk tolerance to some extent. It also pushes an impact on individual investment decisions. So the investment advisors should first study an individual’s personality trait and risk tolerance level. Also, the government should take measures to reduce risk tolerance [16]. The most recent research on psychological disorders and personality traits is based on machine learning algorithms such as genetic algorithms, a type of modifying algorithm that uses natural progression strategies to solve generic problems. The genetic algorithms are projected in binary strings. However, this is not the sole form; depending on the issue being deployed, it can also be provided in alternative ways [17]. Furthermore, in the past, researchers presented genetic algorithms for psychological issue prediction and modeling. As a result of the above discussion, the following hypothesis will be trained and evaluated using Python [18].Hypothesis 3a.
Predicting psychological issues through genetic algorithms outperforms all the other technique used in this study.Hypothesis 3b.
Predicting personality traits through genetic algorithms outperforms all the other technique used in this study.


### 6.4. Predicting Psychological Issues and Personality Traits through Support Vector Machines

On a sample of 300, primary research was conducted to study the influence of personality on socially responsible regression analysis and reliability test. They discovered that agreeableness, neuroticism, and openness to agreeableness control financial decisions in regard to socially responsible investment [19]. Support vector machines are supervised learning models with related learning algorithms that examine data for classification and regression analysis in machine learning. Vladimir Vapnik and his colleagues created it in 1974 at AT&T Bell laboratories. Several researchers conducted psychological problem prediction using support vector machines [20, 21] and discovered that it was suitable for measuring patterns in data. The authors believe the following hypothesis.Hypothesis 4a.
Predicting psychological issues through support vector machine algorithm outperforms all the other technique used in this study.Hypothesis 4b.
Predicting personality traits through support vector machine algorithm outperforms all the other technique used in this study.


## 6.1. Predicting Psychological and Personality Health Issues through Neural Networks

In order to find the psychological issues and personality traits of successful human being for improving the overall performance, a study was conducted in the USA in which they used psychological issues and personality traits as independent variables and performance as the dependent variable. It was found that psychological issues (stress, anxiety, depression, phobias on various forms, and cognitive issues) and personality traits (conscientiousness, openness to experience, and extroverts) are highly skilled, or we can say that they are overconfident in their skills. On the other hand, neuroticism and openness to experience look towards more returns. Openness to experience and neuroticism were found more in successful investors. A neural network is a series of algorithms that aspires to acknowledge underlying relationships in a data set through a procedure that imitates the way human brain operates. Researchers in the past used neural network technique for psychological issue prediction [12]. In this regard, neural network approach has been introduced to discriminant analysis for predicting human psychological issue that leads to organizational performance. Researchers compared the results with linear classifier, logistic regression, KNN, and ID3 and found it to be an accurate method of measurement. Moreover, according to [13], results of psychological issue model constructed by neural networks outperformed model constructed by multiple discriminant analysis. Keeping the above discussion, the authors will suppose the following hypothesis for testing and contribution.Hypothesis 1a.
Predicting psychological issues through neural network algorithm outperforms all the other technique used in this study.Hypothesis 1b.
Predicting personality traits through neural network algorithm outperforms all the other technique used in this study.


## 6.2. Predicting Psychological Issues and Personality Traits through Decision Tree Model

The literature is saturated with many findings that discover statistical association among psychiatric difficulties, big 5 personality traits, and risk-taking attitude in preadolescents. Using multiple regression using questionnaires as data, it was discovered that extroverts and openness to experience are strongly associated with risk-taking attitudes, whereas conscientiousness, neuroticism, and agreeableness cannot predict risk-taking attitudes. Thus, this study found that a higher level of extroverts and openness to experience, as well as a lower level of conscientiousness, is associated with a higher level of risk-taking attitude. As discussed in previous lines with a traditional methods, researcher’s also used the decision tree model. So here, the decision tree model is a type of arithmetical calculation in which algorithms are build and those algorithms are said to be decision tree models in which inquiries and examinations are done in a calculative manner so that they may aid in holding subsequent tests. [14] used decision tree model for psychological test and organization risk measurement and got 91.2% accuracy result. Similarly, [15] also trains and tests the personality traits and psychological issue data to predict psychological issue indicators using random forest decision tree and obtains 90% average prediction accuracy. Thus, keeping the above discussion as a benchmark, the research develops and assumed the following hypothesis to work on.Hypothesis 2a.
Predicting psychological issues through decision tree algorithm outperforms all the other technique used in this study.Hypothesis 2b.
Predicting personality traits through decision tree algorithm outperforms all the other technique used in this study.


## 6.3. Predicting Psychological Issues and Personality Traits through Genetic Algorithms

In the past, traditional methodologies produced a large body of literature on the prediction of psychiatric problems and personality traits. For instance, by using multiple regression, it was discovered that personality trait influences financial risk tolerance to some extent. It also pushes an impact on individual investment decisions. So the investment advisors should first study an individual’s personality trait and risk tolerance level. Also, the government should take measures to reduce risk tolerance [16]. The most recent research on psychological disorders and personality traits is based on machine learning algorithms such as genetic algorithms, a type of modifying algorithm that uses natural progression strategies to solve generic problems. The genetic algorithms are projected in binary strings. However, this is not the sole form; depending on the issue being deployed, it can also be provided in alternative ways [17]. Furthermore, in the past, researchers presented genetic algorithms for psychological issue prediction and modeling. As a result of the above discussion, the following hypothesis will be trained and evaluated using Python [18].Hypothesis 3a.
Predicting psychological issues through genetic algorithms outperforms all the other technique used in this study.Hypothesis 3b.
Predicting personality traits through genetic algorithms outperforms all the other technique used in this study.


## 6.4. Predicting Psychological Issues and Personality Traits through Support Vector Machines

On a sample of 300, primary research was conducted to study the influence of personality on socially responsible regression analysis and reliability test. They discovered that agreeableness, neuroticism, and openness to agreeableness control financial decisions in regard to socially responsible investment [19]. Support vector machines are supervised learning models with related learning algorithms that examine data for classification and regression analysis in machine learning. Vladimir Vapnik and his colleagues created it in 1974 at AT&T Bell laboratories. Several researchers conducted psychological problem prediction using support vector machines [20, 21] and discovered that it was suitable for measuring patterns in data. The authors believe the following hypothesis.Hypothesis 4a.
Predicting psychological issues through support vector machine algorithm outperforms all the other technique used in this study.Hypothesis 4b.
Predicting personality traits through support vector machine algorithm outperforms all the other technique used in this study.


## 7. Research Method of the Study

The Python programming language is exhibiting itself as one of the most admired and used languages for scientific computing. Its advance-level collective nature and its vast spreading capability of comprehending scientific analogies make it a captivating choice for algorithmic development and exploratory data analysis [22]. The types of data required by the researcher and the research design are being followed for the selection of data collection method, i.e., primary data and secondary data, so this study requires the collection of primary data in order to directly obtain information from the people of Pakistan [23]. In this cross-sectional data, we used questionnaires as a data gathering instrument that require less expertise [24], and responses were captured using 5-point Likert scales. The target population is normally associated with the total number of individuals living in a particular country [25]. Therefore, in our study, target population is all literate individuals from nonfinancial companies in Pakistan. So, the following techniques will be used in predication based on algorithms developed through Python. We used the following supervised AI techniques used in model like support vector machine, i.e., radial basic function, logistic regression, decision tree model, and artificial neural network.


### 7.1. Performance Evaluation of ML Techniques

Two well-known performance evaluation metrics are used which are root mean square error (RMSE) and mean absolute error (MAE).


### 7.2. Root Mean Square Error

In equation (1), the RMSE is used for measuring the error rate of the regression models. It is an effective metric to compare the forecasting errors as follows:
(1)RMSE=∑i=1ny∧i−yin2,where n is the number of test samples, yi is the true target value of the ith sample, and yi_cap is the forecasted value by the regressor.


### 7.3. Mean Absolute Error

In equation (2), MAE is a performance metric used to evaluate the performance of a regressor. It is calculated as follows:
(2)MAE=∑i=1nyi−xin,where n is the number of test samples, yi is the true target value of the ith sample, xi is the forecasted value by the regressor, and . represents the absolute value.


## 7.1. Performance Evaluation of ML Techniques

Two well-known performance evaluation metrics are used which are root mean square error (RMSE) and mean absolute error (MAE).


## 7.2. Root Mean Square Error

In equation (1), the RMSE is used for measuring the error rate of the regression models. It is an effective metric to compare the forecasting errors as follows:
(1)RMSE=∑i=1ny∧i−yin2,where n is the number of test samples, yi is the true target value of the ith sample, and yi_cap is the forecasted value by the regressor.


## 7.3. Mean Absolute Error

In equation (2), MAE is a performance metric used to evaluate the performance of a regressor. It is calculated as follows:
(2)MAE=∑i=1nyi−xin,where n is the number of test samples, yi is the true target value of the ith sample, xi is the forecasted value by the regressor, and . represents the absolute value.


## 8. Results and Discussion

Table1 shows the results of different AI techniques in method column, together with evaluation metrics columns used in this study. All the evaluation techniques showed in Table 1 are the source of selecting the best AI technique that will outperform all the other AI technique for predicting the psychological issues and personality traits for improving the overall performance that will help in attaining the organizational vision and mission. In order to know the performance of the machine learning techniques, we used the following evaluation metrics that will outperform the best prediction methods. The evaluation metrics include accuracy, precision, recall, F1 score, and confusion matrix.Table 1
Psychological issue model.
MethodAccuracyPrecisionRecallF1 scoreConfusion matrixPsychological issues - SVM linear0.9110.9160.9940.917[29 5 1 27]Psychological issues - SVM RBF0.9090.9230.9110.921[30 4 0 28]Psychological issues - logistic regression0.9240.9090.9070.923[29 5 1 27]Psychological issues - decision tree0.8230.8980.8800.890[30 4 4 24]Source: own research.


## 9. Data Analysis and Discussion

### 9.1. Model 1: Using Psychological Issues

First, psychological issue model is developed based on the five different components like stress, anxiety, depression, phobias on various forms, and cognitive issues. Evaluation metric results of all these techniques are shared in table below.In Table1, the prediction percentage for almost all these above performed tests for psychological issue models (like stress, anxiety, depression, phobias on various forms, and cognitive issues) exceeds 90% or is near to 90%. It shows that these models predicted psychological issues and all its components like stress, anxiety, depression, phobias on various forms, and cognitive issues with high accuracy and with not much significant errors. Also, Table 2 reflects psychological issues, models, and ANN results.Table 2
Psychological issue model and ANN results.
MethodLayersNeuronAccuracyPrecisionRecallF1 scoreConfusion matrixPsychological issues - ANN120.8860.8870.8760.871[26 2 6 28]Psychological issues - ANN140.9020.8550.9230.855[27 1 4 30]Psychological issues - ANN180.9020.8230.9230.839[27 1 4 30]Psychological issues - ANN1160.2740.8710.8790.887[27 1 7 27]Psychological issues - ANN1320.8110.8870.9090.902[27 1 5 29]Psychological issues - ANN1640.8730.8870.9090.87[27 1 5 29]Psychological issues - ANN11280.9380.8550.8910.889[26 2 5 29]Psychological issues - ANN220.9020.8870.8810.354[0 28 0 34]Psychological issues - ANN240.8730.9020.9090.886[27 1 5 29]Psychological issues - ANN280.8540.870.820.879[27 1 11 23]Psychological issues - ANN2160.8860.8530.9090.918[27 1 5 29]Psychological issues - ANN2320.8870.8420.8880.887[25 3 4 30]Psychological issues - ANN2640.9030.8870.9020.853[25 3 3 31]Psychological issues - ANN21280.9030.8790.9020.919[25 3 3 31]Psychological issues - ANN320.5480.9190.8730.902[0 28 0 34]Psychological issues - ANN340.9050.9020.8030.902[26 2 11 23]Psychological issues - ANN380.8340.9020.8760.354[26 2 6 28]Psychological issues - ANN3160.9050.3540.9410.807[28 0 4 30]Psychological issues - ANN3320.9070.8070.9020.874[25 3 3 31]Psychological issues - ANN3640.8270.8740.8670.891[23 5 3 31]Psychological issues - ANN31280.9070.9390.8520.881[23 5 4 30]Psychological issues - ANN420.8870.8410.8430.909[25 3 7 27]Psychological issues - ANN440.8710.8860.8880.821[25 3 4 30]Psychological issues - ANN480.8710.8790.8790.909[27 1 7 27]Psychological issues - ANN4160.9190.9180.920.888[26 2 3 31]Psychological issues - ANN4320.8870.8870.8840.885[24 4 3 31]Psychological issues - ANN4640.8550.8530.8550.854[24 4 5 29]Psychological issues - ANN41280.8870.8870.8840.885[24 4 3 31]Psychological issues - ANN520.5480.2740.8710.943[0 28 0 34]Psychological issues - ANN540.9030.9020.9050.854[26 2 4 30]Psychological issues - ANN580.9110.9010.8790.807[25 3 4 30]Psychological issues - ANN5160.8820.8720.9180.874[24 4 5 29]Psychological issues - ANN5320.8670.8870.8870.891[23 5 6 28]Psychological issues - ANN5640.8820.8560.8530.881[24 4 4 30]Psychological issues - ANN51280.8620.9230.8790.354[24 4 3 31]Source: own research.


#### 9.1.1. Artificial Neural Network and Psychological Issue Model

The results of evaluation metrics of artificial neural networks are shared below.


### 9.2. Model 2: Personality Traits

Model 2 is built on using neuroticism, conscientiousness, extroversion, agreeableness, and openness to experience. The following are the results of evaluation of personality trait model built using neuroticism, conscientiousness, extroversion, agreeableness, and openness to experience.In Table3, the prediction accuracy rates for psychological issue model have quite stark difference in their rates. But logistic regression with least error rates could be considered accurate, and hence, it depicts that personality trait indicators are worst predictors for human being improvement models as they have least accuracy rates. Moreover, Table 4 manifests personality trait-based model results.Table 3
Personality trait-based model.
MethodAccuracyPrecisionRecallF1 scoreConfusion matrixPersonality traits - SVM linear0.6790.6770.6750.676[24 10 10 18]Personality traits - SVM RBF0.9550.9970.9870.967[33 1 3 25]Personality traits - logistic regression0.6110.6040.6020.601[22 12 12 16]Personality traits - decision tree0.9510.9230.9210.946[32 2 2 26]Source: own research.Table 4
Personality trait-based model ANN results.
MethodLayerNeuronAccuracyPrecisionRecallF1 scoreConfusion matrixPersonality traits - ANN120.6810.9380.9380.821[24 4 17 17]Personality traits - ANN140.6430.8890.6590.906[20 8 11 23]Personality traits - ANN180.7740.9380.6910.938[21 7 8 26]Personality traits - ANN1160.7560.3110.7740.952[21 7 9 25]Personality traits - ANN1320.7240.7710.8090.952[21 7 8 26]Personality traits - ANN1640.9310.7820.8220.821[22 6 0 34]Personality traits - ANN11280.9230.7910.9460.906[24 4 0 34]Personality traits - ANN220.6750.9230.9460.938[18 10 11 23]Personality traits - ANN240.6920.9540.9380.545[19 9 10 24]Personality traits - ANN280.7640.9380.6590.766[22 6 8 26]Personality traits - ANN2160.8120.8890.8110.782[24 4 8 26]Personality traits - ANN2320.9510.9470.9290.959[24 4 0 34]Personality traits - ANN2640.8420.8870.8220.967[26 2 5 29]Personality traits - ANN21280.9120.9470.8990.934[24 4 0 34]Personality traits - ANN320.4820.2260.9410.678[28 0 34 0]Personality traits - ANN340.7740.7770.9560.707[19 9 5 29]Personality traits - ANN381111[24 4 10 24]Personality traits - ANN3160.7430.790.7930.707[23 5 8 26]Personality traits - ANN3320.9210.9360.9110.952[23 5 0 34]Personality traits - ANN3640.9250.9520.9560.969[28 0 3 31]Personality traits - ANN31280.7320.7820.9560.638[28 0 0 34]Personality traits - ANN420.6210.6090.6090.657[16 12 12 22]Personality traits - ANN440.6870.6840.6840.707[21 7 13 21]Personality traits - ANN480.8320.8210.9190.821[23 5 6 28]Personality traits - ANN4160.9120.9590.9020.952[25 3 0 34]Personality traits - ANN4320.9420.9050.9020.903[25 3 0 34]Personality traits - ANN4640.9530.8790.3540.903[28 0 2 32]Personality traits - ANN41280.9210.9180.8070.548[27 1 3 31]Personality traits - ANN520.6230.8870.8740.905[25 3 21 13]Personality traits - ANN540.7210.8530.9390.834[19 9 9 25]Personality traits - ANN580.8510.8790.9420.905[23 5 6 28]Personality traits - ANN5160.9210.8820.9120.907[24 4 2 32]Personality traits - ANN5320.9520.850.9010.827[28 0 4 30]Personality traits - ANN5640.9210.8120.8920.907[28 0 3 31]Personality traits - ANN51280.9420.8960.8820.954[28 0 3 31]Source: own research.


#### 9.2.1. Artificial Neural Network Results

The following are the results of artificial neural networks of personality trait-based model.


### 9.3. Using Both Psychological Issue and Personality Trait Indicators

The third model is constructed using both psychological issue and personality trait indicators. The results of evaluation metrics of model are shared below.In Table5, the results show the highest accurate prediction rate to be 97.8%. It does clearly depict that there is a significant improvement in results by using both psychological issue and personality trait indicator for psychological issue indicator prediction.Table 5
Psychological issue+personality trait-based model results.
MethodAccuracyPrecisionRecallF1 scoreConfusion matrixPsychological issue+personality trait SVM linear0.8450.8310.8420.831[26 8 1 27]Psychological issue+personality trait SVM RBF0.9780.9510.9530.959[33 1 1 27]Psychological issue+personality trait logistic regression0.8850.8890.8420.872[27 7 1 27]Psychological issue+personality trait decision tree0.9410.9240.9450.958[33 1 3 25]Source: own research.


#### 9.3.1. Artificial Neural Network Results

Table6 shows the results of artificial neural network technique applied for psychological issue and personality trait indicators as predictors.Table 6
Psychological issue+personality trait-based model ANN results.
MethodLayerNeuronAccuracyPrecisionRecallF1 scoreConfusion matrixPsychological issue+personality trait ANN120.8310.9510.9380.934[22 6 6 28]Psychological issue+personality trait ANN140.8310.7140.9380.934[26 2 7 27]Psychological issue+personality trait ANN180.8360.8870.950.953[27 1 4 30]Psychological issue+personality trait ANN1160.8450.9190.9380.934[27 1 2 32]Psychological issue+personality trait ANN1320.8510.9510.50.274[27 1 3 31]Psychological issue+personality trait ANN1640.8310.9670.7930.79[27 1 1 33]Psychological issue+personality trait ANN11280.8310.9510.8940.892[27 1 2 32]Psychological issue+personality trait ANN220.9670.9510.9380.934[24 4 15 19]Psychological issue+personality trait ANN240.9530.8070.950.934[25 3 4 30]Psychological issue+personality trait ANN280.950.9510.9380.934[26 2 3 31]Psychological issue+personality trait ANN2160.8030.950.9380.938[27 1 2 32]Psychological issue+personality trait ANN2320.8880.9670.9670.935[27 1 1 33]Psychological issue+personality trait ANN2640.9050.950.9380.548[27 1 2 32]Psychological issue+personality trait ANN21280.920.9530.9670.79[26 2 1 33]Psychological issue+personality trait ANN320.9350.8110.9190.887[26 2 11 23]Psychological issue+personality trait ANN340.9520.8860.9680.935[25 3 4 30]Psychological issue+personality trait ANN380.9350.9020.935[26 2 4 30]Psychological issue+personality trait ANN3160.5480.9180.952[26 2 3 31]Psychological issue+personality trait ANN3320.790.9670.935[27 1 1 33]Psychological issue+personality trait ANN3640.8870.9340.952[27 1 3 31]Psychological issue+personality trait ANN31280.9350.9670.9360.967[27 1 1 33]Psychological issue+personality trait ANN420.9520.8870.8910.889[26 2 5 29]Psychological issue+personality trait ANN440.9030.9020.9020.902[25 3 3 31]Psychological issue+personality trait ANN480.9350.9350.9350.935[26 2 2 32]Psychological issue+personality trait ANN4160.9350.9360.9510.936[27 1 3 31]Psychological issue+personality trait ANN4320.9350.3540.950.936[27 1 3 31]Psychological issue+personality trait ANN4640.9520.7910.9670.951[26 2 1 33]Psychological issue+personality trait ANN41280.9670.8930.950.912[27 1 3 31]Psychological issue+personality trait ANN520.9510.9360.9530.902[0 28 0 34]Psychological issue+personality trait ANN540.9510.9510.8110.924[23 5 8 26]Psychological issue+personality trait ANN580.8070.9360.8860.943[27 1 6 28]Psychological issue+personality trait ANN5160.9510.9510.9020.891[27 1 3 31]Psychological issue+personality trait ANN5320.950.953210.90.871[26 2 1 33]Psychological issue+personality trait ANN5640.9670.9340.9380.906[27 1 3 31]Psychological issue+personality trait ANN51280.950.950.9530.921[27 1 2 32]Source: own research.


## 9.1. Model 1: Using Psychological Issues

First, psychological issue model is developed based on the five different components like stress, anxiety, depression, phobias on various forms, and cognitive issues. Evaluation metric results of all these techniques are shared in table below.In Table1, the prediction percentage for almost all these above performed tests for psychological issue models (like stress, anxiety, depression, phobias on various forms, and cognitive issues) exceeds 90% or is near to 90%. It shows that these models predicted psychological issues and all its components like stress, anxiety, depression, phobias on various forms, and cognitive issues with high accuracy and with not much significant errors. Also, Table 2 reflects psychological issues, models, and ANN results.Table 2
Psychological issue model and ANN results.
MethodLayersNeuronAccuracyPrecisionRecallF1 scoreConfusion matrixPsychological issues - ANN120.8860.8870.8760.871[26 2 6 28]Psychological issues - ANN140.9020.8550.9230.855[27 1 4 30]Psychological issues - ANN180.9020.8230.9230.839[27 1 4 30]Psychological issues - ANN1160.2740.8710.8790.887[27 1 7 27]Psychological issues - ANN1320.8110.8870.9090.902[27 1 5 29]Psychological issues - ANN1640.8730.8870.9090.87[27 1 5 29]Psychological issues - ANN11280.9380.8550.8910.889[26 2 5 29]Psychological issues - ANN220.9020.8870.8810.354[0 28 0 34]Psychological issues - ANN240.8730.9020.9090.886[27 1 5 29]Psychological issues - ANN280.8540.870.820.879[27 1 11 23]Psychological issues - ANN2160.8860.8530.9090.918[27 1 5 29]Psychological issues - ANN2320.8870.8420.8880.887[25 3 4 30]Psychological issues - ANN2640.9030.8870.9020.853[25 3 3 31]Psychological issues - ANN21280.9030.8790.9020.919[25 3 3 31]Psychological issues - ANN320.5480.9190.8730.902[0 28 0 34]Psychological issues - ANN340.9050.9020.8030.902[26 2 11 23]Psychological issues - ANN380.8340.9020.8760.354[26 2 6 28]Psychological issues - ANN3160.9050.3540.9410.807[28 0 4 30]Psychological issues - ANN3320.9070.8070.9020.874[25 3 3 31]Psychological issues - ANN3640.8270.8740.8670.891[23 5 3 31]Psychological issues - ANN31280.9070.9390.8520.881[23 5 4 30]Psychological issues - ANN420.8870.8410.8430.909[25 3 7 27]Psychological issues - ANN440.8710.8860.8880.821[25 3 4 30]Psychological issues - ANN480.8710.8790.8790.909[27 1 7 27]Psychological issues - ANN4160.9190.9180.920.888[26 2 3 31]Psychological issues - ANN4320.8870.8870.8840.885[24 4 3 31]Psychological issues - ANN4640.8550.8530.8550.854[24 4 5 29]Psychological issues - ANN41280.8870.8870.8840.885[24 4 3 31]Psychological issues - ANN520.5480.2740.8710.943[0 28 0 34]Psychological issues - ANN540.9030.9020.9050.854[26 2 4 30]Psychological issues - ANN580.9110.9010.8790.807[25 3 4 30]Psychological issues - ANN5160.8820.8720.9180.874[24 4 5 29]Psychological issues - ANN5320.8670.8870.8870.891[23 5 6 28]Psychological issues - ANN5640.8820.8560.8530.881[24 4 4 30]Psychological issues - ANN51280.8620.9230.8790.354[24 4 3 31]Source: own research.


### 9.1.1. Artificial Neural Network and Psychological Issue Model

The results of evaluation metrics of artificial neural networks are shared below.


## 9.1.1. Artificial Neural Network and Psychological Issue Model

The results of evaluation metrics of artificial neural networks are shared below.


## 9.2. Model 2: Personality Traits

Model 2 is built on using neuroticism, conscientiousness, extroversion, agreeableness, and openness to experience. The following are the results of evaluation of personality trait model built using neuroticism, conscientiousness, extroversion, agreeableness, and openness to experience.In Table3, the prediction accuracy rates for psychological issue model have quite stark difference in their rates. But logistic regression with least error rates could be considered accurate, and hence, it depicts that personality trait indicators are worst predictors for human being improvement models as they have least accuracy rates. Moreover, Table 4 manifests personality trait-based model results.Table 3
Personality trait-based model.
MethodAccuracyPrecisionRecallF1 scoreConfusion matrixPersonality traits - SVM linear0.6790.6770.6750.676[24 10 10 18]Personality traits - SVM RBF0.9550.9970.9870.967[33 1 3 25]Personality traits - logistic regression0.6110.6040.6020.601[22 12 12 16]Personality traits - decision tree0.9510.9230.9210.946[32 2 2 26]Source: own research.Table 4
Personality trait-based model ANN results.
MethodLayerNeuronAccuracyPrecisionRecallF1 scoreConfusion matrixPersonality traits - ANN120.6810.9380.9380.821[24 4 17 17]Personality traits - ANN140.6430.8890.6590.906[20 8 11 23]Personality traits - ANN180.7740.9380.6910.938[21 7 8 26]Personality traits - ANN1160.7560.3110.7740.952[21 7 9 25]Personality traits - ANN1320.7240.7710.8090.952[21 7 8 26]Personality traits - ANN1640.9310.7820.8220.821[22 6 0 34]Personality traits - ANN11280.9230.7910.9460.906[24 4 0 34]Personality traits - ANN220.6750.9230.9460.938[18 10 11 23]Personality traits - ANN240.6920.9540.9380.545[19 9 10 24]Personality traits - ANN280.7640.9380.6590.766[22 6 8 26]Personality traits - ANN2160.8120.8890.8110.782[24 4 8 26]Personality traits - ANN2320.9510.9470.9290.959[24 4 0 34]Personality traits - ANN2640.8420.8870.8220.967[26 2 5 29]Personality traits - ANN21280.9120.9470.8990.934[24 4 0 34]Personality traits - ANN320.4820.2260.9410.678[28 0 34 0]Personality traits - ANN340.7740.7770.9560.707[19 9 5 29]Personality traits - ANN381111[24 4 10 24]Personality traits - ANN3160.7430.790.7930.707[23 5 8 26]Personality traits - ANN3320.9210.9360.9110.952[23 5 0 34]Personality traits - ANN3640.9250.9520.9560.969[28 0 3 31]Personality traits - ANN31280.7320.7820.9560.638[28 0 0 34]Personality traits - ANN420.6210.6090.6090.657[16 12 12 22]Personality traits - ANN440.6870.6840.6840.707[21 7 13 21]Personality traits - ANN480.8320.8210.9190.821[23 5 6 28]Personality traits - ANN4160.9120.9590.9020.952[25 3 0 34]Personality traits - ANN4320.9420.9050.9020.903[25 3 0 34]Personality traits - ANN4640.9530.8790.3540.903[28 0 2 32]Personality traits - ANN41280.9210.9180.8070.548[27 1 3 31]Personality traits - ANN520.6230.8870.8740.905[25 3 21 13]Personality traits - ANN540.7210.8530.9390.834[19 9 9 25]Personality traits - ANN580.8510.8790.9420.905[23 5 6 28]Personality traits - ANN5160.9210.8820.9120.907[24 4 2 32]Personality traits - ANN5320.9520.850.9010.827[28 0 4 30]Personality traits - ANN5640.9210.8120.8920.907[28 0 3 31]Personality traits - ANN51280.9420.8960.8820.954[28 0 3 31]Source: own research.


### 9.2.1. Artificial Neural Network Results

The following are the results of artificial neural networks of personality trait-based model.


## 9.2.1. Artificial Neural Network Results

The following are the results of artificial neural networks of personality trait-based model.


## 9.3. Using Both Psychological Issue and Personality Trait Indicators

The third model is constructed using both psychological issue and personality trait indicators. The results of evaluation metrics of model are shared below.In Table5, the results show the highest accurate prediction rate to be 97.8%. It does clearly depict that there is a significant improvement in results by using both psychological issue and personality trait indicator for psychological issue indicator prediction.Table 5
Psychological issue+personality trait-based model results.
MethodAccuracyPrecisionRecallF1 scoreConfusion matrixPsychological issue+personality trait SVM linear0.8450.8310.8420.831[26 8 1 27]Psychological issue+personality trait SVM RBF0.9780.9510.9530.959[33 1 1 27]Psychological issue+personality trait logistic regression0.8850.8890.8420.872[27 7 1 27]Psychological issue+personality trait decision tree0.9410.9240.9450.958[33 1 3 25]Source: own research.


### 9.3.1. Artificial Neural Network Results

Table6 shows the results of artificial neural network technique applied for psychological issue and personality trait indicators as predictors.Table 6
Psychological issue+personality trait-based model ANN results.
MethodLayerNeuronAccuracyPrecisionRecallF1 scoreConfusion matrixPsychological issue+personality trait ANN120.8310.9510.9380.934[22 6 6 28]Psychological issue+personality trait ANN140.8310.7140.9380.934[26 2 7 27]Psychological issue+personality trait ANN180.8360.8870.950.953[27 1 4 30]Psychological issue+personality trait ANN1160.8450.9190.9380.934[27 1 2 32]Psychological issue+personality trait ANN1320.8510.9510.50.274[27 1 3 31]Psychological issue+personality trait ANN1640.8310.9670.7930.79[27 1 1 33]Psychological issue+personality trait ANN11280.8310.9510.8940.892[27 1 2 32]Psychological issue+personality trait ANN220.9670.9510.9380.934[24 4 15 19]Psychological issue+personality trait ANN240.9530.8070.950.934[25 3 4 30]Psychological issue+personality trait ANN280.950.9510.9380.934[26 2 3 31]Psychological issue+personality trait ANN2160.8030.950.9380.938[27 1 2 32]Psychological issue+personality trait ANN2320.8880.9670.9670.935[27 1 1 33]Psychological issue+personality trait ANN2640.9050.950.9380.548[27 1 2 32]Psychological issue+personality trait ANN21280.920.9530.9670.79[26 2 1 33]Psychological issue+personality trait ANN320.9350.8110.9190.887[26 2 11 23]Psychological issue+personality trait ANN340.9520.8860.9680.935[25 3 4 30]Psychological issue+personality trait ANN380.9350.9020.935[26 2 4 30]Psychological issue+personality trait ANN3160.5480.9180.952[26 2 3 31]Psychological issue+personality trait ANN3320.790.9670.935[27 1 1 33]Psychological issue+personality trait ANN3640.8870.9340.952[27 1 3 31]Psychological issue+personality trait ANN31280.9350.9670.9360.967[27 1 1 33]Psychological issue+personality trait ANN420.9520.8870.8910.889[26 2 5 29]Psychological issue+personality trait ANN440.9030.9020.9020.902[25 3 3 31]Psychological issue+personality trait ANN480.9350.9350.9350.935[26 2 2 32]Psychological issue+personality trait ANN4160.9350.9360.9510.936[27 1 3 31]Psychological issue+personality trait ANN4320.9350.3540.950.936[27 1 3 31]Psychological issue+personality trait ANN4640.9520.7910.9670.951[26 2 1 33]Psychological issue+personality trait ANN41280.9670.8930.950.912[27 1 3 31]Psychological issue+personality trait ANN520.9510.9360.9530.902[0 28 0 34]Psychological issue+personality trait ANN540.9510.9510.8110.924[23 5 8 26]Psychological issue+personality trait ANN580.8070.9360.8860.943[27 1 6 28]Psychological issue+personality trait ANN5160.9510.9510.9020.891[27 1 3 31]Psychological issue+personality trait ANN5320.950.953210.90.871[26 2 1 33]Psychological issue+personality trait ANN5640.9670.9340.9380.906[27 1 3 31]Psychological issue+personality trait ANN51280.950.950.9530.921[27 1 2 32]Source: own research.


## 9.3.1. Artificial Neural Network Results

Table6 shows the results of artificial neural network technique applied for psychological issue and personality trait indicators as predictors.Table 6
Psychological issue+personality trait-based model ANN results.
MethodLayerNeuronAccuracyPrecisionRecallF1 scoreConfusion matrixPsychological issue+personality trait ANN120.8310.9510.9380.934[22 6 6 28]Psychological issue+personality trait ANN140.8310.7140.9380.934[26 2 7 27]Psychological issue+personality trait ANN180.8360.8870.950.953[27 1 4 30]Psychological issue+personality trait ANN1160.8450.9190.9380.934[27 1 2 32]Psychological issue+personality trait ANN1320.8510.9510.50.274[27 1 3 31]Psychological issue+personality trait ANN1640.8310.9670.7930.79[27 1 1 33]Psychological issue+personality trait ANN11280.8310.9510.8940.892[27 1 2 32]Psychological issue+personality trait ANN220.9670.9510.9380.934[24 4 15 19]Psychological issue+personality trait ANN240.9530.8070.950.934[25 3 4 30]Psychological issue+personality trait ANN280.950.9510.9380.934[26 2 3 31]Psychological issue+personality trait ANN2160.8030.950.9380.938[27 1 2 32]Psychological issue+personality trait ANN2320.8880.9670.9670.935[27 1 1 33]Psychological issue+personality trait ANN2640.9050.950.9380.548[27 1 2 32]Psychological issue+personality trait ANN21280.920.9530.9670.79[26 2 1 33]Psychological issue+personality trait ANN320.9350.8110.9190.887[26 2 11 23]Psychological issue+personality trait ANN340.9520.8860.9680.935[25 3 4 30]Psychological issue+personality trait ANN380.9350.9020.935[26 2 4 30]Psychological issue+personality trait ANN3160.5480.9180.952[26 2 3 31]Psychological issue+personality trait ANN3320.790.9670.935[27 1 1 33]Psychological issue+personality trait ANN3640.8870.9340.952[27 1 3 31]Psychological issue+personality trait ANN31280.9350.9670.9360.967[27 1 1 33]Psychological issue+personality trait ANN420.9520.8870.8910.889[26 2 5 29]Psychological issue+personality trait ANN440.9030.9020.9020.902[25 3 3 31]Psychological issue+personality trait ANN480.9350.9350.9350.935[26 2 2 32]Psychological issue+personality trait ANN4160.9350.9360.9510.936[27 1 3 31]Psychological issue+personality trait ANN4320.9350.3540.950.936[27 1 3 31]Psychological issue+personality trait ANN4640.9520.7910.9670.951[26 2 1 33]Psychological issue+personality trait ANN41280.9670.8930.950.912[27 1 3 31]Psychological issue+personality trait ANN520.9510.9360.9530.902[0 28 0 34]Psychological issue+personality trait ANN540.9510.9510.8110.924[23 5 8 26]Psychological issue+personality trait ANN580.8070.9360.8860.943[27 1 6 28]Psychological issue+personality trait ANN5160.9510.9510.9020.891[27 1 3 31]Psychological issue+personality trait ANN5320.950.953210.90.871[26 2 1 33]Psychological issue+personality trait ANN5640.9670.9340.9380.906[27 1 3 31]Psychological issue+personality trait ANN51280.950.950.9530.921[27 1 2 32]Source: own research.


## 10. Final Summary

Tables7 and 8 depict the different models used in the comparison. The above-mentioned results clearly depict that the model containing both psychological issue and personality trait indicators performed better than model containing alone the personality trait or psychological issue indicators. Therefore, it can be noted that psychological issues and personality traits are not the best indicators alone.Table 7
Final summary.
SVM linear: FR-based model>FR+personality trait-based model>personality trait-based modelSVM RBF: FR+ personality trait-based model>FR-based model>personality trait-based modelLogistic regression: FR-based model>FR+personality trait-based model>personality trait-based modelDecision tree: FR+personality trait-based model>personality trait-based model>FR-based modelANN: FR+personality trait-based model>FR-based model>personality trait-based modelTable 8
Comparison of techniques used. Source: own research.
Psychological issue-based model: ANN>logistic regression>SVM linear>decision tree>SVM RBFPersonality trait-based model: ANN>SVM RBF>decision tree>SVM linear>logistic regressionPsychological issue-based model+personality trait-based model: ANN>SVM RBF>decision tree>SVM linear>logistic reg


## 11. Comparison of Technique

Subject to the result tables, it can be concluded the ANN is deemed to have least error rate according to confusion matrix results followed by SVN RBF, decision tree, SVM linear, and logistic regression. Its main reason could be that in different layer of artificial neural networks, different combinations of features were used and improved the efficiency of the model.


## 12. Conclusions

The main aim of this research was to develop the algorithms for prediction of employee health using both psychological health issue and personality health trait indicators. The model developed using the combined data of both psychological issue and personality trait indicators outperformed the model developed using either psychological issue data individual or personality trait indicators for prediction. The model is predicted using five (5) psychological issue indicators and five personality trait indicators. The supervised artificial intelligence techniques used in this study are support vector machine, SVM RBF, logistic regression, decision tree model, and artificial neural networks. The percentage of accuracy of model increased up to 96.4% that makes it an outstanding model for prediction. Using alone psychological issue indicators for prediction displayed dismal results. Our results are in consensus with the study conducted in Taiwan [26], but contrary to research conducted in the USA [2]. Artificial neural networks are deemed to be the best performing technique among other supervised techniques as it has highest accuracy rate and least error rate. This model with positive results would serve as a beacon of light for initiating artificial intelligence in the field of human resource for uplifting the best human capital of Pakistan. To broaden its scope and verify its results, this research can also be conducted on other similar capacity countries to authenticate its results further or identify changes in results in relation to different markets and understanding this variation in relation to them. This study was conducted for predicting psychological issue indicator model; in a similar manner, a model can be constructed for predicting behavior of fraudulent companies using previous data of companies that committed fraud.



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*Source: 5775640-2022-09-17.xml*