Mental Stress Prediction Using Machine Learning on Real Time Dataset

Abstract

Mental stress is a significant issue affecting individuals across all age groups in today's society. The impact of stress extends beyond mental well-being and can lead to various chronic illnesses, including depression, malignancy, and cardiovascular disease (CVD). Addressing mental stress and implementing strategies for stress management and prevention are crucial not only for mental well-being but also for reducing the risk of associated chronic illnesses. In this paper, we are focussing of the problem of mental stress in today's society and the significance of early prediction and management. The use of Random Forest (RF) method with enhanced band pass filtration technique on ECG data for predicting stress levels is a promising approach. Achieving a 96.73% accuracy in stress categorization, along with improvements over prior research results, highlights the effectiveness of the proposed model. The main contribution of the research work is that the results of the model are validated using real-time datasets which further strengthens its reliability and applicability in practical scenarios.