Comparative Analysis of Machine Learning Techniques for Mental Health Prediction

Abstract

The prevalence of mental health problems has prompted investigations into the use of machine learning to tackle the issues. Mental health is a crucial component of an individual’s overall well-being and can be detected and treated early on, significantly improving the quality of life for those affected. This study examines the use of machine learning algorithms to predict mental health disorders using a dataset of self-reported information. Four commonly used machine learning models K-nearest neighbor classifier, logistic regression, random forest and decision tree are compared in terms of their performance. The objective of this study is to compare the performance of these machine learning algorithms on a self-reported mental health dataset and identify the most suitable model for predicting mental health. The challenges faced by the system include the limited size and quality of the dataset, the need for ethical considerations in handling sensitive mental health information, and potential biases in the data .The results of the experiments identify the most suitable model for predicting mental health.