An Improved Bagging Ensemble in Predicting Mental Disorder using Hybridized Random Forest - Artificial Neural Network Model

Oluwashola David Adeniji, Samuel Oladele Adeyemi, Sunday Adeola Ajagbe


Machine Learning majorly provides the process of collecting, identifying, pre-processing, training, validating 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 IT industry. The objective of the study is to develop a Hybrid Random Forest (RF) and Artificial Neural Network (ANN) model to predict mental health disorders among employees in an 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 algorithms 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 employees developing 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 performances 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 results obtained in this study. 

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P. Sandhya and K. Mahek, "Prediction of Mental Disorder for employees in IT Industry. IJITEE (International Journal of Innovative Technology and Exploring Engineering, 8(6), 2278-3075.," IJITEE (International Journal of Innovative Technology and Exploring Engineering, vol. 8, no. 6, pp. 2278-3075, 2019.

S. S. Lim, T. Vos, A. D. Flaxman, G. Danaei, K. Shibuya, H. Adair-Rohani, M. A. AlMazroa, M. Amann, H. R. Anderson and K. G. Andrews, "A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990 – 2010: a systematic analysis for the Global Burden of Disease Study 2010," A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990The Lancet, vol. 380, pp. A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990 2224-2260, 2012.

S. A. Ajagbe and A. O. Adesina, "Design and Development of an Access Control Based Electronic Medical Records (EMR)," Centrepoint Journal (Science Edition), vol. 26, no. 1, pp. 98-119, 2020.

J. F. Dipnall, J. A. Pasco, M. Berk, L. J. Williams, S. Dodd, F. N. Jacka and D. Meyer, "Fusing data mining, machine learning and traditional statistics to detect biomarkers associated with depression," PloS one, vol. 11, no. 2, pp. J. F. Dipnall, J. A. Pasco, M. Berk, L. J. Williams, S. Dodd, F. N. Jacka, and D. Meyer. (2016) “Fusing data mining, machine le148-195, 2016.

J. Han, J. Pei and M. Kamber, "Data Mining: Concepts and Techniques," Elsevier, 2011.

J. B. Awotunde, S. A. Ajagbe, M. O. Oladipupo, J. A. Awokola, O. S. Afolabi, M. O. Timothy and Y. J. Oguns, "An Improved Machine Learnings Diagnosis Technique for COVID-19 Pandemic Using Chest X-ray Images," in Applied Informatics. ICAI 2021. Communications in Computer and Information Science, Springer, Cham, 2021, p. 14555.

I. R. Idowu, O. D. Adeniji, S. Elelu and T. O. Adefisayo, "Prediction of Breast Cancer Images Classification Using Bidirectional Long Short Term Memory and Two-Dimensional Convolutional Neural network," Transactions on Networks and Communications, vol. 9, no. 4, pp. 29-38, 2021.

R. G. Jimoh, O. A. Abisoye and M. M. B. Uthman, "Ensemble feed-forward neural network and support vector machine for prediction of multiclass malaria infection," Journal of Information and Communication Technology, vol. 21, no. 1, pp. 117-148, 2022.

T. M. Awoyelu, A. R. Iyanda and S. K. Mosaku, "Formulation of a Predictive Model for the Determination of Depression among University Students," in Proceeding of the 14th International Conference on Smart Nations, Digital Economies and Meaningful Lives, 2016.

S. Wang, C. Aggarwal and H. Liu, "RandomForest-Inspired Neural Networks," ACM Transmission Intelligence Systems Technology, 9, 6(69), vol. 9, no. 6, pp. 1-25, 2018.

G. Chen, S. Li, H. Long, X. Zeng, P. Kang and H. Zhang, "A Hybrid Algorithm Introducing Cross Mutation and Non-linear Learning Factor for Optimal Allocation of DGs and Minimizing Annual Network Loss in the Distribution Network," IAENG International Journal of Applied Mathematics, vol. 51, no. 3, pp. 1-18, 2021.

P. Gilbert, Depression: The Evolution of Powerlessness, Routledge, London, United Kingdom, 2016.

L. A. Cooper, J. J. Gonzales, J. J. Gallo, K. M. Rost, L. S. Meredith, L. V. Rubenstein, N. Y. Wang and D. E. Ford, "The Acceptability of Treatment for Depression among African American, Hispanic, and White Primary Care Patients. Medical Care," Medical Care, vol. 41, no. 4, 2003.

M. Kantardzic, "Data Mining: Concepts, Models, Methods, and Algorithms," John Wiley and Sons, 2011.

P. Groves, B. Kayyali, D. Knott and S. V. Kuiken, "The Big Data Revolution in Healthcare," 2013.

M. R. Sumathi and B. Poorna, "Prediction of Mental Health Problems Among Children Using Machine Learning Techniques," International Journal of Advanced Computer Science and Applications (IJACSA), vol. 7, no. 1, 2016.

G. Azar, C. Gloster, N. El-Bathy, S. Yu, R. H. Neela and I. Alothman, "Intelligent Data Mining and Machine Learning for Mental Health Diagnosis using Genetic Algorithm," in 2015 IEEE International Conference on Electro/Information Technology (EIT), 2015.

S. Chauhan and A. Garg, "Predictive Research for Mental Health Disease," Blue Eyes Intelligence Engineering & Sciences Publication, 2019.

G. Orrù, W. Pettersson-Yeo, A. F. Marquand, G. Sartori and A. Mechelli, "Using Support Vector Machine to Identify Imaging Biomarkers of Neurological and Psychiatric Disease: A Critical Review," Neuroscience and Biobehavioral Reviews, vol. 36, p. 1140–1152, 2012.

L. Jing, "Mental Health Evaluation Model based on Fuzzy Neural Network," in 2016 International Conference on Smart Grid and Electrical, 2016.

F. Sadeque, D. Xu and S. Bethard, "UArizona at the CLEF eRisk Pilot Task: Linear and Recurrent Models for Early Depression Detection," in In CEUR workshop proceedings, volume 1866. NIH Public Access, 2017.

M. Srividya, S. Mohanavalli and N. Bhalaji, "Behavioral Modelling for Mental Health using Machine Learning Algorithms," Journal of Medical Systems, 2018.

A. Khan, M. H. Husain and A. Khan, "Analysis of Mental State of Users using Social Media to Predict Depression: A Survey," in Conference Paper at the International Journal of Advanced Research in Computer Science, 2018.


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