Sleep Based Stress Level Prediction using Machine Learning

Author(s)	Mr. Mallarapu Venkat Sai, Ms. N Musrat Sultana, Dr. V Subba Ramaiah, Dr. K Rajitha
Country	India
Abstract	Sleep-based Stress Level Prediction Using Machine Learning is a system that evaluates an individual's stress level by analyzing physiological sleep parameters, including snoring intensity, respiration rate, body temperature, limb movement, blood oxygen saturation, eye movement, sleep duration, and heart rate. Machine learning algorithms are applied to preprocessed data to classify stress levels with high accuracy and generalization. Unlike traditional self-reporting methods, this system continuously monitors sensor-based physiological data, enabling objective and real-time stress detection. The use of decision-tree-based classifiers ensures model interpretability for both users and healthcare professionals. The system can be integrated into wearable devices and health monitoring applications, contributing to early stress detection, preventive mental wellness, and personalized healthcare.
Keywords	Sleep-Based Stress Prediction, Machine Learning, Random Forest Classifier, Physiological Parameters, Decision Tree.
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 17, Issue 2, April-June 2026
Published On	2026-04-25

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