Predictive Analysis of Stress Levels Using Explainable Machine Learning Techniques for Stress Management Recommendations

Abstract

Stress has become a significant concern affecting the mental health and performance of individuals, particularly in academically intensive environments. Early prediction and management of stress levels can help in reducing its adverse effects. This study proposes a data-driven approach for the predictive analysis of stress levels using explainable machine learning techniques. Various machine learning algorithms, including Random Forest, Support Vector Machine, and Extreme Gradient Boosting, are employed to build predictive models based on behavioral, academic, and psychological factors.

The proposed framework integrates explainable artificial intelligence methods to interpret model predictions and identify key stress-inducing factors. In addition to prediction, the study incorporates a recommendation mechanism that suggests appropriate stress management strategies based on predicted stress levels. The performance of the models is evaluated using standard metrics such as accuracy, precision, recall, and F1-score.

The results demonstrate that ensemble-based models provide higher predictive accuracy compared to traditional approaches. Furthermore, the use of explainable techniques enhances the transparency and reliability of the model. The proposed system can assist educators and individuals in early identification of stress and enable timely interventions through personalized recommendations.