Measuring Sleep Stages and Quality Based on K-Means and Random Forest Algorithms

The purpose of this study is to measure sleep quality based on k-means and random forest algorithms in a big data environment. Firstly, the sleeping data of users is clustered into different stages of sleep using the k-means algorithm. Then, the random forest algorithm is used to predict sleep health indicators based on the clustering results. Health prediction based on sleeping stage is of high accuracy and reliability. Our study provides a new method for users to better understand their sleep quality and health status.