Comparative Analysis of Machine Learning Algorithms for Detection of the Stress of Humans During Sleep
Keywords:
Machine Learning, Stress, Repeated Stratified K-Fold, Cross Validation and Classifiers.Abstract
Machine learning (ML) is an emerging technology that is used for machines to act like humans. It has vast applications in all domains such as healthcare, agriculture, and industries. This paper is focused on the healthcare domain specifically for the detection of the stress of the human, while their sleeping. Stress comes in two flavors: eustress and distress. Chronic anguish can cause major health problems. Adrenaline and cortisol are two important hormones that are involved in the body's stress response. Accurate detection methods are necessary for stress management that works. The goal of stress detection models is to improve both individual and societal health. Health depends on the ability to recognize stress while you sleep, and physiological and machine-learning data indicate promise in this area. Much research has been done on the detection of stress by using machine learning algorithms. High accuracies of 96.83% to 100% are attained by using a variety of classifiers, including Random Forest, KNN, and Logistic Regression. Here the Performance of the model is improved by cross-validation techniques such as Repeated Stratified K-Fold. Here the results of the various ML algorithms before and after applying the cross-validation have been discussed and compared. Few algorithms were shown effective after and before applying the cross-validation. To achieve even greater benefits, future research might concentrate on feature engineering and ensemble techniques. Developing dependable stress detection systems is the ultimate objective.
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