Chintalapati, A.; Annamalai, R.; Enkhbat, K.; Ozaydin, F.; Sivashanmugam, K. Enhancing Stress Identification Using Machine Learning: Revealing Key Factors with SHAP- Driven Explainable AI. Preprints2024, 2024092135. https://doi.org/10.20944/preprints202409.2135.v1
APA Style
Chintalapati, A., Annamalai, R., Enkhbat, K., Ozaydin, F., & Sivashanmugam, K. (2024). Enhancing Stress Identification Using Machine Learning: Revealing Key Factors with SHAP- Driven Explainable AI. Preprints. https://doi.org/10.20944/preprints202409.2135.v1
Chicago/Turabian Style
Chintalapati, A., Fatih Ozaydin and Karthikeyan Sivashanmugam. 2024 "Enhancing Stress Identification Using Machine Learning: Revealing Key Factors with SHAP- Driven Explainable AI" Preprints. https://doi.org/10.20944/preprints202409.2135.v1
Abstract
The accurate detection and assessment of stress play a pivotal role in enhancing individual well-being and healthcare outcomes. Traditional methods of stress detection often grapple with limitations in accuracy and scalability. With the advent of machine learning (ML), the potential to revolutionize stress detection has emerged. This paper presents a comprehensive study on the application of ML algorithms for stress detection, with a focus on physiological and behavioral data analysis. Central to our approach is the integration of SHapley Additive exPlanations (SHAP), an Explainable Artificial Intelligence (XAI) technique, to interpret ML models. SHAP provides a novel lens to understand the impact of individual features in the complex decision-making processes of ML models, thereby enhancing the transparency and reliability of stress predictions. We demonstrate how SHAP not only aids in elucidating model decisions but also contributes to refining the models for greater accuracy. Our results highlight the effectiveness of ML in detecting stress and the pivotal role of XAI in making these models more interpretable and trustworthy. This study underscores the synergy between advanced ML techniques and XAI, paving the way for more nuanced and reliable stress detection methodologies that are essential in diverse settings, from healthcare to workplace environments.
Keywords
Stress Detection; Machine Learning; Explainable AI
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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