# Deep Recurrent Neural Network Assisted Stress Detection System for Working Professionals
## 1 Introduction  
The working conditions of professionals in India are deteriorating at an alarming rate in modern society. As per the Automatic Data Processing (ADP) Research Institute's most recent reports for 2020, 70% of Indian workers are stressed out at work, which is a major cause for concern. There has been a rise in the number of professionals who are sad or at risk of suicide as a result of increased stress according to surveys conducted by 1 to 1 help, an Employee Assistance Program provider in India. In the year 2017, OSMI, also known as Open Sourcing Mental Illness, was in effect. There were 750 responses to the survey from staff members. The dataset initially consisted of 68 features drawn from both the subjects' personal and professional lives. Optum, a NASSCOM-affiliated company, found that nearly half of Indian workers were experiencing stress or mental health issues. Seventy significant corporations, each with a maximum and a minimum of 4000 and 500 employees, respectively, were questioned. The findings were frightening, revealing a two-year rise in the percentage of employees at high risk of suicide-related stress between 40% and 80%. They also discovered that the most common sources of stress for workers were relationships with family and finances [1; 2; 3; 4]. These mechanisms [5; 6; 7; 8] are primarily concerned with predicting the moods of working professionals based on their job performance, their mental state before and after work hours, and participation in office activities.  
Machine learning is typically one of the most widely used categorization models for tackling specific prediction issues [9; 10]. Artificial Intelligence (AI) models include Support Vector Machines (SVMs), Neural Networks (NNs), Relevance Vector Machines(RVMs), Naive Bayes (NB), Decision Trees (DTs), Logistic Regression (LR), and K-Nearest Neighbor (KNN). Deep-learning-based classification [11; 12; 13; 14] models, when compared to these techniques, deliver efficient and accurate performance results for any complicated challenge. Long Short-Term Memory (LSTM), Deep Neural Networks (DNNs), Convolutional Neural Networks (CNNs), and Deep Belief Networks (DBNs) are all included. Deep-learning-based categorization techniques are more suitable for properly predicting the mindset and stress levels of working professionals in the workplace because they offer accurate prediction outcomes, an increased performance rate, efficiency, and decreased time consumption. As a result, the goal of this research is to use an advanced deep learning model to anticipate working professionals' stress levels based on their comments. The original dataset is preserved here, along with several features of the working professionals' information and feedback. This makes use of prediction sources that have quickly been processed to accurately predict working professionals' stress levels. The following are the contributions of this research:  
* A deep learning model based on a DRNN is used to accurately identify whether professionals are anxious.
* The given dataset is preprocessed by removing the unnecessary set of attributes and filling in the missing values to obtain a normalized dataset and by also extracting and choosing the most appropriate number of features from the pre-processed dataset to train the data model in order to improve the classifier's prediction performance.
* Various assessment metrics are validated to assess the effectiveness of the proposed prediction mechanism, including accuracy, precision, recall, sensitivity, specificity, error rate, and F1-score.  
The outline of this research article is as follows: Section 2 discusses the background of the research; Section 3 presents the methodology of the research work; Section 4 covers the results and discussion; Section 5 covers the conclusion.