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Predicting Mental Health Illness using Machine Learning Algorithms

Journal of physics. Conference series, 2022-01, Vol.2161 (1), p.12021 [Peer Reviewed Journal]

Published under licence by IOP Publishing Ltd ;Published under licence by IOP Publishing Ltd. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1742-6588 ;EISSN: 1742-6596 ;DOI: 10.1088/1742-6596/2161/1/012021

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  • Title:
    Predicting Mental Health Illness using Machine Learning Algorithms
  • Author: Vaishnavi, Konda ; Nikhitha Kamath, U ; Ashwath Rao, B ; Subba Reddy, N V
  • Subjects: Accuracy ; Algorithms ; Classifiers ; Decision trees ; Machine learning ; Mental health ; Stacking
  • Is Part Of: Journal of physics. Conference series, 2022-01, Vol.2161 (1), p.12021
  • Description: 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%.
  • Publisher: Bristol: IOP Publishing
  • Language: English
  • Identifier: ISSN: 1742-6588
    EISSN: 1742-6596
    DOI: 10.1088/1742-6596/2161/1/012021
  • Source: Geneva Foundation Free Medical Journals at publisher websites
    IOPscience (Open Access)
    Institute of Physics IOP eJournals Open Access
    ProQuest Central
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