MACHINE LEARNING FRAMEWORK FOR DETECTION OF MENTAL STRESS

Abstract: Mental stress is becoming a common issue among students and individuals due to factors such as workload, lack of sleep, screen time, deadline pressure, financial concerns, personal responsibilities, and limited relaxation time. Identifying stress levels can be difficult because different factors can affect each individual differently. This research presents a machine learning framework for the detection of mental stress using questionnaire-based data. Data was collected from 51 respondents through a Google Form and included factors such as sleep hours, screen time, workload, physical activity, socialization, sleep quality, deadline pressure, financial concerns, personal or family responsibilities, and social support. The collected data was processed and used to calculate a stress score, based on which respondents were classified into Low, Moderate, and High stress levels. The dataset contained 17 columns and the stress-level distribution included 10 Low, 37 Moderate, and 4 High stress responses. A machine learning classification model was developed using Python to identify stress-level patterns. The model achieved an accuracy of approximately 81.82%. The study also used data visualization to understand the distribution of stress and the relationship between different factors and stress levels. The proposed framework shows how data analysis and machine learning can be used as a preliminary approach for detecting and understanding mental stress patterns.

Keywords: Mental Stress, Machine Learning, Stress Detection, Stress Level, Data Analysis, Python, Classification