Behavioral Modeling for Mental Health using Machine Learning Algorithms

• Published in: Journal of medical systems Vol. 42; no. 5; pp. 88 - 12
• Main Authors: Srividya, M., Mohanavalli, S., Bhalaji, N.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.05.2018; Springer Nature B.V
• Subjects: Adolescents; Algorithms; Anxiety; Artificial intelligence; Bayesian analysis; Children; Classifiers; Clustering; Colleges & universities; Convergence of Deep Machine Learning and Nature Inspired Computing Paradigms for Medical Informatics; Decision trees; Drug abuse; Drug addiction; Education & Training; Health Informatics; Health problems; Health Sciences; K-nearest neighbors algorithm; Labels; Learning algorithms; Machine learning; Medicine; Medicine & Public Health; Mental depression; Mental disorders; Mental health; Mental health care; Obsessive compulsive disorder; Occupational health; Predictive analytics; Regression analysis; Social interactions; Statistics for Life Sciences; Students; Support vector machines; Target groups; Well being; Predictive analytics; Behavioral healthcare; Mental health; Classification
• ISSN: 0148-5598; 1573-689X; 1573-689X
• DOI: 10.1007/s10916-018-0934-5

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.