Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
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All Journal Journal Informatic, Education and Management (JIEM)
Dirga Febrian
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Education Languange, Linguistic, Communication & Media Other

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Machine Learning Models for Predicting Mental Health Indicators Using Digital Physical Activity Data: A Systematic Literature Review Dirga Febrian; Rolly Maulana Awangga
Journal Informatic, Education and Management (JIEM) Vol 7 No 2 (2025): AUGUST
Publisher : STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61992/jiem.v7i2.142

Abstract

This systematic literature analysis examines 40 studies (2020–2025) on the use of machine learning to predict mental health using digital activity data. Two research questions are presented: algorithm performance comparison and model effectiveness factor. Data surveys (43,9%) are a more widely used data collection method. Because of its interpretability, Logistic Regression is the most popular (29.3%), whereas Random Forest (26.8%) is best for performance-interpretability. With a rata-rata accuracy of 80.1% ± 4.2% and an AUC of 87.1% ± 1.8%, XGBoost provides superior performance. The best study achieves an AUC >0,98 through feature engineering that canggih using SHAP and recursive feature elimination. Critical success factors include cermat fitur selection, temporal dinamika, cross-validation, and clinical interpretability. Although machine learning has significant potential, there are still challenges with standardization, generalizability, and real-world implementation. Research in the long term requires longitudinal studies, external validation, and standard protocols to realize this technology's potential in improving mental health outcomes.
Co-Authors Rolly Maulana Awangga, Rolly Maulana