Investigations on Machine Learning Models for Mental Health Analysis and Prediction

Abstract

Machine learning Techniques are identified as the most suitable methods for mental health analysis and prediction. Mental illness among people has increased vastly around the world and has become a serious human problem to be solved. From much research work and research articles, it is evident that machine learning algorithms can be an effective approach to finding mental illness. In this paper, different machine learning algorithms are investigated to find the best model, suitable to predict the mental health of a person more accurately and at a faster rate. In order to create a system that operates effectively and quickly, this paper investigates the performance of various machine learning models, including KNN, Support Vector Machine, Random Forest, Logistic regression, Decision tree, etc. All the models are compared based on the accuracy that each method offers after successful execution.