A Comparative study: Mental Patient Disorder Classification Using Text Mining

Abstract

A key element of community well-being is mental health, which is influenced by social and organizational contexts in which people live and work in addition to personality traits. In spite of research on the mental health conditions of specific populations, there hasn't been much effort to concentrate on developing strategies for identifying and assessing the effects of mental health problems. In this article, we will use supervised machine learning algorithms to detect mental health. The data set we use is from Kaggle. We employed logistic regression, K nearest neighbor, and random forest, among other supervised machine learning techniques. To find the best performance, we compare and evaluate model results.