چکیده مقاله
Background and Objective: The rising prevalence of depression and limited access to mental health services underscore the need for machine learning algorithms in early diagnosis and prevention The objective of this study is to conduct a systematic review to evaluate the applications and effectiveness of these algorithms in improving mental health outcomes Methods: A systematic review was conducted following PRISMA guidelines by searching PubMed, Scopus, Web of Science and IEEE Xplore for articles published from 2014 to 2025 After screening and applying inclusion criteria, 38 high quality studies were selected Results: Sixty eight percent of studies focused on depression diagnosis, 55% on prevention, and 40% on continuous mental health monitoring Data sources comprised electronic questionnaires, textual analyses, and behavioral data Deep neural networks, especially transformer models like BERT and GPT, achieved accuracy between 85% and 91% in predicting depressive symptoms 10 to 12 days before clinical diagnosis These models effectively identified hidden behavioral patterns and enabled ongoing mental health monitoring Conclusion: Machine learning algorithms are powerful tools for diagnosing and preventing depression; however, challenges such as data privacy and methodological standardization remain Future studies should aim to improve accuracy and develop ethical frameworks Keywords: Machine learning, depression, early detection, mental health, neural networks
کلیدواژهها
نویسندگان
Elnaz Bornasi
Master's student in Health Information Technology, Student Research Committee, Lorestan University of Medical Sciences, Khorramabad, Iran
شیوه ارجاع
Bornasi, Elnaz,1404,A Systematic Review of Machine Learning Algorithms for Early Detection and Prevention of Depression,20th International Conference on Health, Treatment and Health Promotion
ارائهشده در
مجموعه مقالات بیستمین کنفرانس بین المللی بهداشت، درمان و ارتقای سلامت24 مرداد 1404