Classification of Depression, Internet Addiction and Prediction of Self-esteem among University Students

Abstract

Machine learning is massively used in the prediction of cognitive and psychological features in recent times. This research aims to find the predictability between leading disorders like Internet addiction, depression, and low self-esteem. For this purpose, 461 undergraduate students have been selected arbitrarily from several educational institutions of Dhaka city and voluntarily completed a standard questionnaire that was prepared based on the self-reported measures concerning the disorders mentioned above. Different standard psychometric scales such as Internet Addiction Test (IAT) by Dr. Kimberly Young, Self-esteem Scale by M. Rosenberg, PROMIS Emotional Distress Depression short-scale by PROMIS Health Organization have been used in the correlational survey. The internal consistency of the data has been proven by Cronbach alpha. Subsequently, the Shapiro-Wilk Normality test revealed the data to be non-parametric. Several essential features have been extracted to reduce the redundancy from the data using minimum-redundancy-maximum-reduction (mRMR) and Chi-square test. A prediction model has been devised using Logistic Regression, Naive Bayes, Random Forest, C4.5 Decision Tree, and k-Nearest Neighbors. The experimental result shows that Internet addiction and depression are interconnected with self-esteem, and thereby, the prediction model can be built to reduce the severity of these disorders.