Sleep Stress Level Classification through Machine Learning Algorithms

Abstract

Nowadays, chronic insomnia is a critical problem of homo-sapiens. An increase in workload and tension in life led to the development of sleep stress. Sleep stress can damage human beings in a physical, psychological, and social manner. Sickness in the stomach, tension, and frayed nerves while sleeping are the most frequent symptoms of sleep stress. Sleep stress can lead to cardiac infarction, depression, senile psychosis, gastrointestinal problems, diabetes, obesity, and emphysematous. This paper primarily focuses on the classification of sleep stress levels using standard machine learning algorithms like Decision Tree (DT), Logistic Regression (LR), Radial basis function Supported-Vector Classifier (RBF-SVC), K-Nearest Neighbor (KNN), Random Forest (RF), Extreme Gradient Boosting (XGB), Linear Support-Vector Classifier (L-SVC), Naive Bayes (NB), Support-Vector Classifier (SVC), on the scaled dataset using Standard Scaling. LR, KNN, and SVC outperformed all the other machine learning classifiers in terms of performance metrics.