Developing Smart Process for Predicting Mental illness like Depression, Anxiety, and Stress using Machine Learning Methods

Abstract

Depression, anxiety, and stress are growing problems worldwide with 3.8% of people experiencing depression and 4.4% dealing with anxiety. These concerns are particularly widespread in Pakistan, affecting 20 million individuals in the country. The treatment of mental illness is hindered by stigma, a scarcity of mental healthcare resources, prolonged consultations, and the expensive fees associated with consulting psychologists and psychiatrists. The objective of this study is to develop machine learning (ML) prediction models for mental illness issues like depression, anxiety and stress among Pakistani students. The Depression Anxiety Stress Scale 21 (DASS21) is utilized to gather data from 115 students. A study has introduced a novel process of data generation considering the utilization of multinomial probability distribution with correlation. The use of chi-square test and Recursive Feature Elimination (RFE) with random forest reveals that all 21 features of DASS21 (depression, anxiety, and stress) exhibit statistical significance. The prediction models are developed using five machine learning algorithms i.e., random forest, decision tree, support vector Machines, naive bayes, and k-nearest neighbors. A comparison reveals SVM outperforms other models with an F1 Score of 0.97, 0.96 and 0.97 for depression, anxiety and stress. The proposed models could be used in a practical framework to facilitate the quick screening decision support system for depression, anxiety, and stress.