Machine Learning Approaches for Early Detection of Mental Health Disorders Using Wearable Devices and Big Data Analytics
1 Department of Computer Engineering, Olabisi Onabanjo University, Ago-Iwoye, Nigeria.
2 Department of Structural Engineering, University of Benin, Benin City.
3 Department of Nursing Sciences, Nnamdi Azikiwe University, Awka, Nigeria.
4 Department of Mathematics and Statistics (Data Science) CAS, American University, Washington DC.
5 Department of Information Sciences, Bay Atlantic University, United States.
6 Department of Health Informatics, Indiana University Indianapolis, Indiana, USA.
Review
International Journal of Biological and Pharmaceutical Sciences Archive, 2025, 10(02), 006-023.
Article DOI: 10.53771/ijbpsa.2025.10.2.0077
Publication history:
Received on 03 September 2025; revised on 11 October 2025; accepted on 13 October 2025
Abstract:
The escalating global burden of mental health disorders, such as depression, anxiety, and schizophrenia, underscores the urgent need for innovative early detection strategies to improve outcomes and reduce healthcare costs. This review explores the transformative potential of integrating wearable devices, big data analytics, and machine learning to identify prodromal signs of mental health conditions with unprecedented precision. Wearable technologies, capturing physiological and behavioral biomarkers like heart rate variability and sleep patterns, synergize with diverse big data sources, including electronic health records and social media, to fuel advanced machine learning models, supervised, unsupervised, deep learning, and federated approaches, that predict disorders with remarkable accuracy. By synthesizing cutting-edge research, this paper illuminates how multimodal data fusion and real-time processing pipelines enable scalable, personalized monitoring systems. However, challenges such as data quality, model interpretability, privacy concerns, and regulatory hurdles must be addressed to translate these innovations into clinical practice. Emerging trends, including edge AI and personalized interventions, promise to enhance accessibility and equity, paving the way for a future where proactive mental health care transforms lives. This review offers a comprehensive roadmap for researchers and clinicians to harness these technologies, driving impactful advancements in early detection and intervention.
Keywords:
Machine Learning; Wearable Devices; Big Data Analytics; Mental Health; Early Detection; Biomarkers; Data Fusion; Privacy
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Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0