Stress and Anxiety early Detection with Machine Learning Approach using Survey Data

Abstract

Stress and anxiety can have profound impacts on an individual's mental and physical well-being, making their early detection crucial for timely intervention and support. This study investigates the application of machine learning for detecting stress and anxiety levels from survey data, which is crucial for timely intervention and support. A comprehensive experimental analysis evaluated various algorithms, including Support Vector Machines (SVM), XGBoost, CatBoost, ensemble methods like Random Forest, and traditional techniques like Logistic Regression. The experimental setup utilized powerful computational resources and employed tools like NumPy, Pandas, and Scikit-learn. Extensive data preprocessing and feature engineering ensured data quality. Results revealed SVM, XGBoost, and CatBoost as top performers, achieving highest accuracy and F1 scores, demonstrating effectiveness in capturing complex stress and anxiety patterns. However, challenges like data quality, feature selection, individual variability, and interpretability were encountered. The study highlights machine learning's potential for stress and anxiety detection, providing insights into algorithm selection and performance trade-offs. Future work may focus on incorporating domain knowledge, addressing ethical considerations, and deploying the model for improved mental health support.