Research for the Effects of Sleep Quality Based on Multiple Linear Regression and Random Forest Model

Sleep efficiency is a key factor in human health. However, existing research tends to focus on isolated variables, ignoring the intricate relationship between sleep patterns and daily habits. In this paper, the Sleep Efficiency Dataset of the Kaggle platform was selected to analyze the effects of sleep structure and lifestyle factors on sleep efficiency. Through multiple linear regression models, the relationship between sleep duration, deep sleep percentage, light sleep percentage, rapid eye movement (REM) sleep percentage, and number of wake-ups was explored. The results showed that deep sleep and REM sleep percentage had a significant positive effect on sleep efficiency, while the number of wake-ups had a negative effect. The random forest model further analyzed the effect of lifestyle on sleep efficiency, and the results showed that age, alcohol consumption, and smoking status were the main factors affecting sleep efficiency. The results of the study provide data to support the improvement of sleep efficiency.