Mental Stress Prediction Using Machine Learning

In today's fast-paced world, mental stress has become a significant issue, contributing to numerous health issues and lower productivity. Early detection and prediction of stress levels can help individuals take timely preventive measures. Analyzing physiological, behavioral, and questionnaire-based data, this project presents a machine learning-based method for predicting mental stress. The system leverages various machine learning algorithms, including Support Vector Machines (SVM), Random Forest, and Logistic Regression, to classify stress levels into different categories such as low, moderate, and high. Heart rate, sleep patterns, physical activity, and responses to psychological assessment scales (such as the DASS-21 or Perceived Stress Scale) are among the data features used for prediction. The dataset is preprocessed for noise reduction, normalization, and missing value handling. Feature selection techniques such as PCA and correlation analysis are applied to enhance model performance. In order to guarantee the models' robustness and generalizability, k-fold cross-validation is used to train and validate them. The Random Forest algorithm is a promising tool for real-time mental health monitoring because it predicts stress levels with the highest accuracy, as demonstrated by experimental results. This study demonstrates the potential for continuous stress monitoring and mental health support by combining machine learning with wearable sensors and mobile apps.