Abstract
Abstract: Stress is a prevalent issue that affects individuals' mental and physical well-being, leading to various health problems. The use of machine learning (ML) has been gaining popularity as a tool for stress detection. ML techniques have shown promising results in identifying patterns and features from various physiological and behavioral data sources such as heart rate, blood pressure, and speech signals. The primary goal of stress detection using ML is to provide accurate, non-invasive, and costeffective methods for early stress detection and intervention. Overall, stress detection using ML holds great promise in providing an objective, efficient, and scalable approach for stress monitoring and intervention. Further research is required to address the challenges associated with data collection, feature extraction, and model generalization to diverse populations and contexts.
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