Leveraging Explainable AI and Multimodal Data for Stress Level Prediction in Mental Health Diagnostics

Agboro Destiny
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Agboro Destiny: University of Hertfordshire, United Kingdom

International Journal of Research and Innovation in Applied Science, 2024, vol. 9, issue 12, 416-425

Abstract: The increasing prevalence of mental health issues, particularly stress, has necessitated the development of data-driven, interpretable machine learning models for early detection and intervention. This study leverages multimodal data, including activity levels, perceived stress scores (PSS), and event counts, to predict stress levels among individuals. A series of models, including Logistic Regression, Random Forest, Gradient Boosting, and Neural Networks, were evaluated for their predictive performance. Results demonstrated that ensemble models, particularly Random Forest and Gradient Boosting, performed significantly better compared to Logistic Regression. Random Forest achieved an accuracy of 73%, while Gradient Boosting delivered a balanced precision-recall tradeoff with an accuracy of 72%. Gradient Boosting outperformed in identifying high-stress instances, achieving a recall of 70%, making it the most reliable model for stress prediction.

Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:bjf:journl:v:9:y:2024:i:12:p:416-425

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