Predicting Mental Health Illness using Machine Learning Algorithms

Konda Vaishnavi, U. Nikhitha Kamath, B. Ashwath Rao, N. V.Subba Reddy

Research output: Contribution to journalConference articlepeer-review

2 Citations (Scopus)

Abstract

Early detection of mental health issues allows specialists to treat them more effectively and it improves patient's quality of life. Mental health is about one's psychological, emotional, and social well-being. It affects the way how one thinks, feels, and acts. Mental health is very important at every stage of life, from childhood and adolescence through adulthood. This study identified five machine learning techniques and assessed their accuracy in identifying mental health issues using several accuracy criteria. The five machine learning techniques are Logistic Regression, K-NN Classifier, Decision Tree Classifier, Random Forest, and Stacking. We have compared these techniques and implemented them and also obtained the most accurate one in Stacking technique based with an accuracy of prediction 81.75%.

Original languageEnglish
Article number012021
JournalJournal of Physics: Conference Series
Volume2161
Issue number1
DOIs
Publication statusPublished - 11-01-2022
Event1st International Conference on Artificial Intelligence, Computational Electronics and Communication System, AICECS 2021 - Manipal, Virtual, India
Duration: 28-10-202130-10-2021

All Science Journal Classification (ASJC) codes

  • Physics and Astronomy(all)

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