Physiological Stress Prediction using Machine Learning Classifiers

Abstract

The aim of this study is to predict the stress of a person using Machine Learning classifiers. This system classifies the stress of a person as either High or Low. There are various classification algorithms present, out of which 9 classification algorithms have been chosen for this study. The algorithms implemented are K-Nearest Neighbor classifier, Support Vector Machine with an RBF kernel, Decision Tree algorithm, Random Forest algorithm, Bagging Classifier, Adaboost algorithm, Voting classifier, Logistic Regression and MLP classifier. The different algorithms are applied on the same dataset. The dataset is obtained from a GitHub repository labelled Stress classifier with AutoML. The different accuracies of each algorithm are found, and the classification algorithm with the best accuracy is determined. On comparison, it was found that the K-Nearest Neighbor algorithm has the best accuracy with an accuracy rate of 79.3% for physiological stress prediction. While other algorithms had varying accuracies, K-Nearest Neighbor algorithm was the most consistent.