Behavioral Modeling for Mental Health using Machine Learning Algorithms
Mental health is an indicator of emotional, psychological and social well-being of an individual. It determines how an individual thinks, feels and handle situations. Positive mental health helps one to work productively and realize their full potential. Mental health is important at every stage of life, from childhood and adolescence through adulthood. Many factors contribute to mental health problems which lead to mental illness like stress, social anxiety, depression, obsessive compulsive disorder, drug addiction, and personality disorders. It is becoming increasingly important to determine the onset of the mental illness to maintain proper life balance. The nature of machine learning algorithms and Artificial Intelligence (AI) can be fully harnessed for predicting the onset of mental illness. Such applications when implemented in real time will benefit the society by serving as a monitoring tool for individuals with deviant behavior. This research work proposes to apply various machine learning algorithms such as support vector machines, decision trees, naïve bayes classifier, K-nearest neighbor classifier and logistic regression to identify state of mental health in a target group. The responses obtained from the target group for the designed questionnaire were first subject to unsupervised learning techniques. The labels obtained as a result of clustering were validated by computing the Mean Opinion Score. These cluster labels were then used to build classifiers to predict the mental health of an individual. Population from various groups like high school students, college students and working professionals were considered as target groups. The research presents an analysis of applying the aforementioned machine learning algorithms on the target groups and also suggests directions for future work.
KeywordsMental health Classification Predictive analytics Behavioral healthcare
Compliance with ethical standards
“All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.”
“Informed consent was obtained from all individual participants included in the study.”
- 1.Miner, L., et al., Practical predictive analytics and decisioning systems for medicine: Informatics accuracy and cost-effectiveness for healthcare administration and delivery including medical research. Cambridge: Academic Press, 2014.Google Scholar
- 2.Luxton, D. D., (ed.) Artificial Intelligence in Behavioral and Mental Health Care. Amsterdam: Elsevier Inc., 2015.Google Scholar
- 5.World Health Organization, Mental health: a call for action by world health ministers. Geneva: World Health Organization, Department of Mental Health and Substance Dependence, 2001.Google Scholar
- 6.Funk, M., Global burden of mental disorders and the need for a comprehensive, coordinated response from health and social sectors at the country level. http://apps.who.int/gb/ebwha/pdf_files/EB130/B130_9-en.pdf. Accessed 20 Feb 2016, 2016.
- 7.Drapeau, A., Marchand, A., and Beaulieu-Prévost, D. Mental illnesses-understanding, prediction and control. Epidemiol. Psychol. Distress, (2012). https://doi.org/10.5772/1235.
- 11.Qiu, T., Zhang, Y., Qiao, D., Zhang, X., Wymore, M. L., & Sangaiah, A. K., A robust time synchronization scheme for industrial internet of things. IEEE Trans. Ind. Inf., 2017. https://doi.org/10.1109/TII.2017.2738842.
- 13.Kumar, P., Kumari, S., Sharma, V., Sangaiah, A. K., Wei, J., and Li, X., A Certificateless aggregate signature scheme for healthcare wireless sensor network. Sustain. Comput. Inf. Syst., 2017. https://doi.org/10.1016/j.suscom.2017.09.002.
- 14.Sangaiah, A. K., Samuel, O. W., Li, X., Abdel-Basset, M., and Wang, H., Towards an efficient risk assessment in software projects–Fuzzy reinforcement paradigm. Comput. Electr. Eng., 2017. https://doi.org/10.1016/j.compeleceng.2017.07.022.
- 15.Wu, F., et al., A lightweight and robust two-factor authentication scheme for personalized healthcare systems using wireless medical sensor networks. Futur. Gener. Comput. Syst. 82:727–737, 2017.Google Scholar
- 16.Aborokbah, M. M., Al-Mutairi, S., Sangaiah, A. K., and Samuel, O. W., Adaptive context aware decision computing paradigm for intensive health care delivery in smart cities—A case analysis. Sustain. Cities Soc. 2017.Google Scholar
- 20.Strauss, J., Peguero, A. M., and Hirst, G., Machine learning methods for clinical forms analysis in mental health. MedInfo. 192:1024, 2013.Google Scholar
- 22.Wang, H., and Wang, J., An effective image representation method using kernel classification. Tools with Artificial Intelligence (ICTAI), 2014 I.E. 26th International Conference on. IEEE, 2014.Google Scholar
- 23.Cheng, X. et al., iATC-mISF: a multi-label classifier for predicting the classes of anatomical therapeutic chemicals. Bioinformatics 33(3):341–346, 2016.Google Scholar
- 28.Smets, E., et al. Comparison of machine learning techniques for psychophysiological stress detection. International Symposium on Pervasive Computing Paradigms for Mental Health. Springer International Publishing, 2015.Google Scholar
- 29.Xu, J., et al. On the properties of mean opinion scores for quality of experience management. Multimedia (ISM), 2011 I.E. International Symposium on. IEEE, 2011.Google Scholar
- 33.Dziopa, T., Clustering Validity Indices Evaluation with Regard to Semantic Homogeneity. FedCSIS Position Papers 2016.Google Scholar
- 34.Aggarwal, C. C., and Zhai, C. X., A survey of text classification algorithms. Mining text data. Springer US, 163–222, 2012.Google Scholar
- 38.Joachims, T., Text categorization with support vector machines: learning with many relevant features. In: European conference on machine learning. Pp. 137–142. Berlin, Heidelberg: Springer, 1998.Google Scholar
- 40.Vlahou, A. et al., Diagnosis of ovarian cancer using decision tree classification of mass spectral data. Biomed. Res. Int. 2003(5):308–314, 2003.Google Scholar
- 42.Jiang, L., et al. Survey of improving k-nearest-neighbor for classification." Fuzzy Systems and Knowledge Discovery, 2007. FSKD 2007. Fourth International Conference on. Vol. 1. IEEE, 2007.Google Scholar
- 44.Liu, B., et al. Scalable sentiment classification for big data analysis using naive bayes classifier. Big Data, 2013 I.E. International Conference on. IEEE, 2013.Google Scholar
- 46.Ribeiro, M. T., Singh, S., and Guestrin, C., Why should i trust you?: Explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2016.Google Scholar
- 47.Kuncheva, L. I. Combining pattern classifiers: methods and algorithms. New York: John Wiley & Sons, 2004.Google Scholar