https://doi.org/10.31449/inf.v46i4.3916 Informatica 46 (2022) 543–550 543 An Improved Bagging Ensemble in Predicting Mental Disorder Using Hybridized Random Forest - Artificial Neural Network Model Oluwashola David Adeniji 1 , Samuel Oladele Adeyemi 2 , Sunday Adeola Ajagbe 3,4* 1 Computer Science Department, University of Ibadan, Ibadan, Nigeria 2 Clinical Nursing Department, University College Hospital, Ibadan, Oyo State 3 Computer Engineering Department, Ladoke Akintola University of Technology, LAUTECH, Ogbomoso, Nigeria 4 Computer and Industrial Production Engineering, First Technical University, Ibadan. E-mail: od.adeniji@ui.edu.ng, samadeyemi10@yahoo.com, saajagbe@pgschool.lautech.edu.ng (Corresponding au- thor’s email) Keywords: Hybrid model, Machine learning (ML), Mental disorder, Mental health, RF-ANN Received: January 16, 2022 Machine Learning majorly provides the process of collecting, identifying, pre-processing, training, validat- ing and visualization of data. This study identifies the problem of late detection of mental disorders in IT employees. There are many cases of mental disorders that are not apparent, notable or diagnosed until they become critical. This affects the productivity of the employees not only in the information technology (IT) industry. The objective of the study is to develop a Hybrid Random Forest (RF) and Artificial Neural Net- work (ANN) model to predict mental health disorders among employees in the IT industry. The experiment applied a hybrid Random Forest and Artificial Neural Network (RF-ANN) model in predicting the chances of IT employees developing mental disorders. To measure the performance of the model, RF and ANN al- gorithms were separately developed, their results were recorded and compared with the results of the hybrid model. In the hybrid model using “Bagging Ensemble,” the prediction of an IT employee developing a Mental Disorder shows the weighted average performance of 84.5% for precision, recall, and accuracy and precision is 82.5% using the hybridized RF and ANN models on “Bagging Ensemble”. This result obtained from the hybrid model correctly shows a significant improvement in its performance over individual per- formances of the RF model and ANN models. There was a marginal improvement in the performance of the hybrid model when compared with the result of the parameter-tuned RF. This suggests that by applying the RF-ANN model an improved dataset could be investigated and compared with the results obtained in this study. Povzetek: Članek se ukvarja z napovedovanjem mentalnih težav s pomočjo metod globokih nevronskih mrež, naključnih gozdov in vrečastega ansambla. 1 Introduction Mental health is defined as a person's psychological, social, and emotional state when they are functioning at an acceptable level of behavioural and emotional adjustment. Mental health can be viewed as a measure of an individu- al's ability to handle stress and make decisions in all as- pects of their life, as it has a significant impact on how such an individual act, thinks and feels. Mental health is an important factor at any stage of life, whether it is adult- hood or childhood [1]. According to the World Health Or- ganization, depression is the leading cause of Mental Health Disorders worldwide, affecting individuals as well as communities. It is estimated that more than 350 million people worldwide suffer from depression as of 2020 [2, 3]. Mental health issues have a significant impact on work- place productivity, not only for the individual but also for the organization as a whole. Unfortunately, people gener- ally find it difficult to discuss mental health issues in pub- lic, and society does not raise enough awareness. The cur- rent evolution of Machine Learning (ML) solutions has re- sulted in automated models that can predict, classify, and diagnose some of the issues associated with mental health disorders. The alarming trend of rising mental health problems, combined with the global inability to find effective solu- tions, was impeding both individual and societal prosper- ity. There were numerous and significant barriers to ac- cessing mental health care, ranging from socioeconomic inequalities to personal stigmas. This provides an oppor- tunity for technology, particularly artificial intelligence (AI)-based technology, to help alleviate the situation and provide numerous unique benefits. Kolenik & Gams, (2021) [4] provided a brief overview of persuasive tech- nology (PT) for mental health, as well as general, tech- nical, and critical thoughts on implementation and impact in terms of potential benefits and risks. While potential benefits identified in the research include; cost, availabil- ity and stigma. Group exclusion and research bias were identified as the PT risk. We believe that such technology can supplement existing mental health care solutions by reducing access inequalities as well as those caused by a lack of it. 544 Informatica 46 (2022) 543–550 S.A. Ajagbe S.A. et al. In recent years, ML techniques have been adopted in numerous medical researches, especially in biomedicine and neuroscience to gain further insight into mental health disorders [5]. Machine learning, being an area of artificial intelligence involves the process of computers learning from data through the use of heuristic algorithms [6, 7]. ML is divided into two types: supervised ML and unsu- pervised ML. Supervised ML models are typically used to assign a set of attributes to a target class, which implies classification and regression. Unsupervised ML models are used to describe the relationship or characteristics of a set of attribute data. Unsupervised ML primarily necessi- tates the processes of feature selection, clustering, and as- sociation rule mining [8]. Studies show that employees in IT industries are at high risk of developing mental disor- ders due to increased stress and pressure to meet targets and deadlines. In many cases, these disorders are not ob- vious, known or diagnosed until they become life-threat- ening. The existing studies in different fields have imple- mented various machine techniques to predict mental dis- orders. However, there is a need to address the issue of late detection of mental disorders in IT employees. Develop- ing automated models that can predict, diagnose and clas- sify mental health disorders is now possible with the help of computer-aided systems [9]. By using these developed models, they help in saving manpower, time and other re- sources, while also removing the possibilities of human bias. A large amount of data is readily available thanks to the advancement in the usage, power and capacity of the latest computer technologies. This has resulted in an in- crease in the ability to collect, store and manipulate data. Subsequently, knowledge can be extracted from the data by bringing out patterns and relationships through the de- velopment of a methodology. Such methodology can be developed from a database of existing tools and methods available for the discovery of knowledge and data mining [10, 11, 12]. A hybrid model of neural network (NN) with a ran- dom forest (RF) structure can produce a result with im- proved generalization ability and accuracy. The ability of this hybrid architecture to reduce the back-propagation al- gorithm to a more powerful and generalized decision tree structure makes it more effective than random forests. In addition, this model is more efficient to train as the num- ber of training examples usually requires only a small con- stant factor [12, 13]. Therefore, this study aims to develop a model that can predict the chances of IT employees de- veloping mental disorders using a hybrid of the two best performing models in previous studies consisting of RF and ANN. The developed hybrid model was evaluated us- ing standard metrics in this study area such as precision, recall, and F1-score. The organization of this paper is as follows: the introduction is in section one, review of the literature is contained in section two. Section three is the methodology and the result and discussion were high- lighted. Finally, the conclusion is contained in section 5. 2 Review of literature Mental health disorders, also known as mental ill- nesses, refer to a variety of mental health conditions that affect a person's thinking, mood, and behavior. Anxiety disorders, depression, addictive behaviors, schizophrenia, eating disorders, and other mental disorders are examples. Many people experience various mental health issues from time to time. However, these mental health issues only be- come a mental disorder when the ongoing signs and symp- toms cause frequent stress and impair a person's ability to function effectively. Loss of pleasure or interest, poor con- centration, loss of appetite, disturbed sleep, feelings of guilt, and low energy are all symptoms of mental health disorders. These problems have the tendency to become chronic and recurrent, and thus impair a person’s ability to take care of their daily responsibilities [14]. According to [15], more than 30% of people suffering from major men- tal disorders do not seek treatment, while more than 80% of people battling with some form of mental disorder do not seek to be treated at all. Variations of mental illnesses Depression, bipolar disorder, schizophrenia and other psy- choses, dementia, and developmental disorders are all ex- amples of mental illnesses. Machine Learning and Healthcare: Precision medicine is a way in which healthcare professionals can move to more personalized care by adopting ML in finding pat- terns and reasons about data [16]. With the large volume of data being collected about patients in the healthcare sec- tor, it is near impossible for humans to analyze. With suf- ficient data and permission to use, there are numerous ways in which ML can be applied in healthcare. In times past, hard-coded software has been developed based on external studies to provide recommendations and alerts for different medical practices. The limitation to this however is the problem with the accuracy of data due to other fac- tors such as location, environment, population, and so on. With ML, data can be refined to a particular environment, for instance, refining data from a hospital and the sur- rounding environment in a way that the patient’s infor- mation is anonymized. Examples of ways in ML can help healthcare providers include: the prediction of a possible outbreak of disease, predicting the possibility of hospital readmission for critically ill patients, prediction of cancer risks in patients, and so on [9]. According to the study by Groves et al. (2013) [17], being able to identify patients that are most liable to the risk of hospital readmission helps healthcare providers to offer better support after discharge. The lives of those at risk are improved when the rate of readmission is lowered, and this can be made possible with the intervention of ML. Implementation of artificial intelligence in healthcare or- ganizations as a response to the needs of doctors to aid the patients in their daily decision-making activities is now on the increase. This hopes to improve decision making and reduce errors. In the long run, it reduces cost and improved workflow and the general well-being of people. An Improved Bagging Ensemble in Predicting Mental Disorder… Informatica 46 (2022) 543–550 545 Related works: The Internet of Things (IoT), which refers to the integra- tion of technology into everyday life and the interconnec- tivity of omnipresent devices, has stymied a dedicated re- search venture in the field of mental health. Recognizing that mental health issues are on the risen, affecting indi- viduals and society in increasingly complex ways and that existing human resources are insufficient to address the crisis, decision-makers have turned to technology to see what opportunities it may provide. The role of IoT-ena- bled technology in this new digital mental health land- scape can be divided into two complementary processes: assessment and intervention [18]. Prediction of mental health problems in children using eight ML techniques, three of which, multilayer percep- tron, multiclass classifier, and logical analysis of data (LAD) tree, produced more accurate results with only a slight difference between their performances over the full attribute set and the selected attribute set. The study found that by developing a high-performing model, early diag- nosis of mental health problems in children will help healthcare professionals to treat it at an earlier stage and subsequently improve the quality of patients’ life. There- fore, there comes an urgent need to treat basic mental health problems that persist among children which may lead to complicated problems, if not treated at an early stage [19]. By introducing a genetic algorithm (GA) in de- veloping a system for intelligent data mining and ML for mental health diagnosis, Azar, et al., (2015) [20] were able to extract keywords from the user’s symptoms. The re- search introduced a new approach that was used for a sem- iautomated system that helps in the preliminary diagnosis of the psychological disorder patient. This was achieved by matching the description of a patient’s mental health status with the mental illnesses. The study constructed a semi-automated system based on an integration of the technology of genetic algorithms, classification data min- ing and ML. The goal was to help psychological analysts make informed, appropriate and intelligent assessments leading to accurate prognoses by ensuring that they are aware of all possible mental health illnesses that could match the patient’s symptoms. The predictive research for mental health disease was proposed in a prototype that used RF classification to de- termine the mental state of a person based on attributes such as lifestyle, age, education, gender, vision, occupa- tion, sleep, personal income, mobility, diabetes and hyper- tension [21]. With the amount of data produced by humans daily and with most of this data stored in a semi-structured way, these researchers believed that by using this ML technique, hidden patterns can be found between the dif- ferent attributes of data. WEKA and RATTLE were used and the result of 83.33 % and 92.85. % accuracy was re- ported. With these, the system would be able to predict whether a patient was suffering from mental illness or not. A critical review using SVM to identify imaging bi- omarkers of neurological and psychiatric disease was con- ducted by [22]. The study provided an overview of the method and reviewed studies that applied SVM in the in- vestigation of schizophrenia, Alzheimer’s disease, Parkin- son’s disease, bipolar disorder, pre-symptomatic Hunting- ton’s disease, major depression, and an autistic spectrum disorder. Standard univariate analysis of neuroimaging data revealed a host of neuroanatomical and functional differences between healthy individuals and patients suf- fering a wide range of psychiatric and neurological disor- ders. The mental health evaluation model based on the fuzzy neural network was carried out by [23] by selecting the important factors such as the input vector, the model was used to evaluate the psychological health of college students in China. The combination of neural networks (NN) and fuzzy mathematics improved the accuracy of the mathematical model compared to other traditional models and made it easy to analyze the overall mental health trend of students. Recurrent and linear models to detect depres- sion early were developed [24]. The goal of the study was to achieve early automatic detection of depression from users’ posts on the social media site – Reddit. For predic- tion, both sequential (RNN) and non-sequential (SVM) models were used. The results showed the superiority of sequential models over nonsequential models. The re- search did not sufficiently explore the broad range of pos- sible features. Different ML techniques such as KNN, SVM, naïve bayes classifier, decision trees, and logistic regression to identify the state of mental health in a target group. The replies to the designed questionnaire from the target group were first exposed to unsupervised learning techniques. The Mean Opinion Score was used to validate the labels obtained by clustering. The cluster labels were then utilized to create classifiers that could predict an in- dividual’s mental health. Population from a wide range of groups such as college students, high school students, and working professionals were considered as target groups. A survey on the analysis of the mental state of social media users to predict depression was conducted by [25]. The survey was done to detect depression and mental ill- ness through the use of social media are surveyed. They found out that there was a very high rate at which depres- sion and mental illness were being diagnosed in recent times. They observed that some symptoms linked to men- tal illness were detectable on Facebook, Twitter, and web forums. They suggested that using automatic methods would help in locating inactivity and other mental dis- eases. Various automated detection methods could help to detect depressed people using social media. Mentally ill users were pointed out through the use of screening sur- veys, their Twitter analysis based on community distribu- tion, or their membership in online forums, and they were detectable through the patterns in their language and online activities. Additionally, they observed that a num- ber of authors experienced that numerous activities on so- cial networking sites could be linked to low self-confi- dence, especially in young people and adolescents. A predictive model for the determination of the risk of depression among university students was also devel- oped by [10]. The study extracted knowledge on the fac- tors causing depression among university students. In the study, a predictive model for depression risk with a view to determining the risk of depression among university students was formulated, simulated and validated. 546 Informatica 46 (2022) 543–550 S.A. Ajagbe S.A. et al. The result of the study identified variables that have strong relevance to developing depression among university stu- dents. The simulation results showed that the model with- S/N Ref Goals Contribution 1 [18] Identify approaches in digital men- tal health Distinguished technology for mental health diagnosis into two complementary processes: assessment and intervention 2 [21] RF (ML) classifier was used along- side predictive models to determine mental health diseases. WEKA and RATTLE were used as predictive models, 83.33 % and 92.85 accuracies were reported. 3 [20] To introduce GA in developing in- telligent data mining and ML sys- tem for mental health diagnosis, was able to extract keywords from the user’s symptoms. Matched the description of a patient’s mental health status with the mental illnesses. The research introduced a new ap- proach that was used for a semiautomated system that helps in the preliminary diagnosis of the psychological disorder patient. 4 [19] Prediction of mental health in chil- dren Multilayer perceptron, Multiclass classifier, and LAD tree outperformed other ML models used. 5 [22] To identify imaging biomarkers of neurological and psychiatric dis- ease using SVM was conducted Method and reviewed studies that applied SVM in the inves- tigation of schizophrenia, Alzheimer’s disease, Parkinson’s disease, bipolar disorder, pre-symptomatic Huntington’s dis- ease, major depression, and an autistic spectrum disorder were reported. 6 [23] To evaluate mental health based on the fuzzy neural network The combination of NNs and fuzzy mathematics improved the accuracy of the mathematical model compared to the ex- isting method. 7 [24] To achieve early automatic detec- tion of depression from users’ posts on the social media site RNN and SVM The approach achieved early automatic detection of depres- sion and ensured superiority of sequential models over nonsequential models 8 [25] to conduct a survey on detecting de- pression and mental illness by the use of social media information. They found out that there was a very high rate at which de- pression and mental illness were being diagnosed in recent times. They observed that some symptoms linked to mental illness were detectable on Facebook, Twitter, and web fo- rums. 9 [10] To depression risk with a view to determining the risk of depression among university students using predictive models 93.7% accuracy was achieved on the ML model used Table 1: Summary of related works out feature selection gave a total of 465 correct classifica- tions out of 507 records with an accuracy of 91.7% while feature selection, gave a total of 475 correct classifications with an accuracy 93.7 %. It has been established that the are research gaps in the area of using ML techniques to provide solutions to some of the issues relating to mental disorders among the IT employee, hence, this study was informed. Table 1 shows the summary of the related works, the goals and their respective contributions 3 Methodology The developed model consists of the phases namely: the dataset pre features extraction (training and testing), and the evaluation results. During the preprocessing phase, values of missing data were replaced and even dis- tribution of data was ensured with features scaling. Fea- tures of the pre-processed data were split into two with 67% of the data set aside for training and the remaining 33% set aside for testing. Using the bagging ensemble method, the training set was passed through hybridized RF and Artificial Neualgorithms, using RF as the base. The performance of the result was then evaluated using the standard evaluation metric namely: precision, recall, f1- score and accuracy. Figure 1 shows the developed meth- odology framework in this research, the framework show- ing the stages followed to achieve the results. The selec- tion gave a total of 475 correct classifications. 3.1 Data collection and implementation The input data for this model is the dataset provided by OSMI Ltd. The dataset is derived from a survey aimed at measuring the attitudes of people towards mental health in IT workplace and examining the rate of mental disor- ders set contains 63 variables or columns and 1,433 re- sponses/ observations. The performances of the models An Improved Bagging Ensemble in Predicting Mental Disorder… Informatica 46 (2022) 543–550 547 built were evaluated on the basis of their precision, recall, and F1-score. To further understand and measure the per- formance of the hybrid model, a different set of algorithms was implemented. These models include: RF with default parameters, RF with tuned parameters and the developed model consists of three different phases namely: the da- taset pre-processing, features extraction (training and test- ing), and the processing phase, values of missing data were replaced, even distribution of data was ensured with features processed data were split into two with 67% of the data set aside for training and the remaining 33% set aside for testing. Using the bagging ensemble method, the training set was passed through hybridized RF and ANN. 4 Results and discussions The performances of these models were observed and compared with the hybrid model. The results obtained from the trained models are discussed below: Random forest technique: The result obtained from the model as shown in Table 2, though the model performance was poor in the classification of IT employee with the sta- tus of mental health disorder with 44%, 44%, and 45% precision, f1 accuracy, recall respectively. The default-pa- rameterized RF model performed at a weighted average of 48% precision, 49% F1-score and 50% recall respectively. This poor performance called for the need to tune the pa- rameter in order to obtain improved performance. This outcome attests to the study of [22]. Table 2: Result of the random forest model Pre- ci- sion F1- Score Re- call Sup- port 0 0.23 0.19 0.17 103 1 0.53 0.53 0.53 183 2 0.56 0.60 0.65 157 Macro average 0.44 0.44 0.45 473 Weighted average 0.48 0.49 0.50 473 Accuracy - 0.50 - 473 Artificial neural networks (ANN) techniques: The re- sult of ANN model is as shown in Table 3 poorly incor- rectly classifying “Employees not sure of their mental health status”. Overall, the model performed at a weighted average of 72% precision, 69% F1 score and 71% recall. Table 3: Result of the ANN Model Preci- sion F1- Score Re- call Sup- port 0 0.52 0.36 0.28 103 1 0.90 0.79 0.70 183 2 0.65 0.77 0.95 187 Macro average 0.69 0.64 0.64 473 Weighted av- erage 0.72 0.69 0.71 473 Accuracy - 0.71 - 473 RF-ANN technique: RF and ANN algorithms were hy- bridized for the Bagging ensemble issue. The hybrid model was trained on the training set and its performance was evaluated on the testing set. The result of the hybrid- ized model in table 4 showed a significant improvement in the performance of the hybrid model over the perfor- mances of the RF model with default parameters and ANN model and a slight improvement over the per parameter- tuned RF ANN model’s weighted average performance was improved by the hybrid model from (72%, 69% and 71%,) to (74%, 72% and 74%) precision, F1-score and re- call respectively. The results are similar to the finding in [23-24]. Table 4: Result of the hybrid model Preci- sion F1- Score Recall Support 0 0.64 0.48 0.38 103 1 0.84 0.79 0.74 183 2 0.69 0.94 0.94 187 Macro average 0.73 0.69 0.68 473 Weighted average 0.74 0.72 0.74 473 Accuracy - 0.74 - 473 Back-propagation is the stage in which the weights are ad- justed based on the loss in an attempt to find an optimal weight. Training an ANN is a process that entails deter- mining the optimal weight that will minimize loss. In do- ing so, "categorical cross-entropy loss" was used to find the loss and "adam" optimization was used to find the op- timal solution, with "accuracy" as a metric for perfor- mance evaluation. As shown, the model was trained with 45 epochs and a batch size of 10. Figures 2 and 3 Depict the training process, with decreasing loss and increasing accuracy as training progresses. 548 Informatica 46 (2022) 543–550 S.A. Ajagbe S.A. et al. Figure 2: The Training loss and the Training Accuracy Against Number of Epochs Figure 3: Training Accuracy versus Training Loss Show- ing Inverse Relationship Findings: From all the results obtained, Table 5 reveals that the hybrid RF and ANN using “Bagging Ensemble” gave the best performance with the weighted average per- formance of 84.5% for precision, recall, and accuracy and precision is 82.5%. It can also be observed that the model is able to correctly predict IT employees suffering from Mental Health Disorders with 97% recall. Additional in- sight has gotten from the results in this experiment reveals that there was only a marginal improvement in the perfor- mance of the hybridized model when compared with the result of the parameter-tuned RF. This shows that RF is one of the best classifiers in the predictive algorithm which is consistent with the work of [21] and [25-28]. Table 5: Result Comparison of the 3 Models Perfor- mance met- rics Re- call (%) Predic- tion accu- racy (%) F1 Score (%) Preci- sion ANN 81.5 81.5 82.5 82.5 RF 60.5 60.5 58.5 58.5 Developed model (RF- ANN) 84.5 84.5 82.5 84.5 A comparison of the existing work with our study is pre- sented in Table 6, showing the comparison of the goals in the existing works with our goals viz-a-viz the respective contributions 5 Conclusion The study described various approaches to predicting mental disorders. It focused on the development of a hy- brid predictive model for determining the risk level of mental disorders among employees in IT industry. Most of the existing models focused on predicting mental disor- ders using a single ML technique. This study identified the variables measured in IT employees which are relevant to the prediction of mental disorders in the dataset collected. The results obtained showed that developing a hybridized RF-ANN model had the best overall performance, hence, our developed model. The study described various ap- proaches to predicting mental disorders. It focused on the development of a hybridized predictive model for deter- mining the risk level of mental disorders among employ- ees in IT industry. Most of the existing models focused on predicting mental disorders using a single ML technique. However, there is also the need to address the issue of late detection of mental disorders in IT employees. This study identified the variables measured in IT employees which are relevant to the prediction of mental disorders in the da- taset collected. In conclusion, the best performing model to predict mental disorders in employees of IT industry has been identified, the work has been able to develop a predictive model based on the most relevant factors causing mental disorders. From all the results obtained the hybrid RF and ANN using “Bagging Ensemble” gave the best perfor- mance in predicting IT employees suffering from Mental Health Disorders with a weighted average performance of 84.5% for precision, recall, and accuracy and precision is 82.5%. meaning there was a marginal improvement in the performance of the hybrid model when compared with the result of the parameter-tuned RF. This suggests that by ap- plying the RF-ANN model an improved dataset could be investigated and compared with the results obtained in this study. In addition, insights gotten from the results in this study reveal that there was only a marginal improvement in the performance of the hybrid model when compared with the result of the parameter-tuned RF. The study is not only contributing to the computing field but also to healthcare delivery. Recommendations The following recommendations are made based on the findings of this study: This model can be adopted as an assistant tool by mental health professionals to help them in making an early and more consistent diagnosis of men- tal disorders. The model can be integrated into an existing employees’ Health Information System (HIS) which has clinical data about the employees. It is recommended that variables monitored in IT employees be reviewed on a reg- ular basis in order to increase the amount of information relevant to developing an improved mental health disorder prediction model. In the future, the hybridized algorithm could be used to predict mental disorders in a variety of fields. Similarly, using RF with parameter tuning on a bet- ter dataset could be investigated and compared to the find- ings of this study. An Improved Bagging Ensemble in Predicting Mental Disorder… Informatica 46 (2022) 543–550 549 Table 6: Comparison of the existing work with our study S/N Ref Goals Contribution 1 [18] Identify approaches in digital mental health Distinguished technology for mental health diagnosis into two complementary processes: assessment and intervention 2 [21] RF (ML) classifier was used alongside predictive models to determine mental health diseases. WEKA and RATTLE were used as predictive models, 83.33 % and 92.85 accuracies were reported. 3 [20] To introduce GA in developing intelligent data mining and ML system for mental health diagnosis, was able to extract key- words from the user’s symptoms. Matched the description of a patient’s mental health status with the mental illnesses. The research intro- duced a new approach that was used for a semiauto- mated system that helps in the preliminary diagnosis of the psychological disorder patient. 4 [19] Prediction of mental health in children Multilayer perceptron, Multiclass classifier, and LAD tree outperformed other ML models used. 5 [22] To identify imaging biomarkers of neuro- logical and psychiatric disease using SVM was conducted Method and reviewed studies that applied SVM in the investigation of schizophrenia, Alzheimer’s disease, Parkinson’s disease, bipolar disorder, pre-sympto- matic Huntington’s disease, major depression, and an autistic spectrum disorder were reported. 6 [23] To evaluate mental health based on the fuzzy neural network The combination of NNs and fuzzy mathematics im- proved the accuracy of the mathematical model com- pared to the existing method. 7 [24] To achieve early automatic detection of depression from users’ posts on the social media site RNN and SVM The approach achieved early automatic detection of depression and ensured superiority of sequential mod- els over nonsequential models 8 [25] to conduct a survey on detecting depres- sion and mental illness by the use of social media information. They found out that there was a very high rate at which depression and mental illness were being diag- nosed in recent times. They observed that some symp- toms linked to mental illness were detectable on Fa- cebook, Twitter, and web forums. 9 [10] To depression risk with a view to deter- mining the risk of depression among uni- versity students using predictive models 93.7% accuracy was achieved on the ML model used. Only accuracy was measured 10 Our study to develop a hybrid model to predict mental health disorders among em- ployees in an IT industry An effective hybrid technique was developed that is capable of predicting the mental health of IT in- dustry employees, the model was also evaluated singly before hybridizing and after hybridization, the hybridized model shows improved perfor- mance than the single ones References [1] P. Sandhya and K. Mahek, "Prediction of Mental Disorder for employees in IT Industry. 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