Prediction of stress levels in sleep patterns based on random forest

Abstract

The prevalence of stress in contemporary society has emerged as a significant concern, exerting a profound influence on our daily lives. The objective of this study is to predict stress levels in sleep patterns through the utilization of a machine learning algorithm known as random forest. The significance of stress detection has increased due to its potential to induce various issues such as insomnia and depression. The examination of stress can assist individuals in mitigating the adverse effects associated with prolonged exposure to stress. The study commences with the preprocessing phase, followed by an exploratory data analysis, subsequent dataset splitting, identification of significant features, and concludes with model training. The utilization of the random forest model can enhance the comprehension of the association between sleeping characteristics and levels of stress. Furthermore, it produces a f1-score of 98 percent, indicating a strong predictive capability for determining stress levels in sleep patterns. The proposed method can effectively predict stress levels during sleep mode. This study can provide an effective model for society to prevent people's psychological problems in advance.