Predictive Modeling of Stress Levels Using Physiological and Lifestyle Factors
DOI:
https://doi.org/10.61779/jasetm.v4i1.6Keywords:
Stress Prediction, Random Forest Classifier, Physiological Factors, Lifestyle Factors, Machine Learning in Healthcare, Feature Importance AnalysisAbstract
Stress has emerged as a critical factor that affects both physical and mental health in today’s fast paced lifestyle. Early detection of stress levels can enable timely interventions and preventive healthcare. This study presents a data driven approach for stress level classification using physiological and lifestyle features derived from a publicly available dataset. Key variables include age, occupation, cholesterol level, sleep quality, physical activity, and other health related metrics. A Random Forest Classifier is used to model and classify individuals into three stress levels, i.e., low, moderate, and high. The model achieves a classification accuracy of 0.74, with an averaged F1 score of 0.71, indicating a strong generalization performance. Feature importance analysis reveals that occupation, age, and cholesterol level are the most influential predictors of stress. Deeper analysis shows that individuals in high response profes sions, within the age range of 30 to 40 years, and with elevated cholesterol levels are more prone to high stress. Moreover, the model offers insight into the relative importance of each feature, enhancing the interpretability and potential clinical utility. The proposed model demonstrates that integrating machine learning with wearable and lifestyle data serves as a powerful tool for proactive stress management and personalized healthcare.
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