Prediction of Daily Smoking Behavior Based on Decision Tree Machine Learning Algorithm

With the accumulation of smoking data and the development of the algorithm, precise analysis become possible and this can benefit smoking cessation a lot. However, as far as we know, little research has been done on the behavior of everyday smoking, such as the precise time when a smoker smokes. This paper proposes a model based on decision tree machine learning algorithm to predict daily smoking time. The simulation data set of smoking time data was established by using the population information of smokers collected by the Chinese center for disease control and prevention. In order to solve the problem of too little feature information, we propose a feature information extraction module. In this paper, we tested a variety of machine learning algorithms, and finally came to the conclusion that the prediction model based on XGBoost had the best performance, with an accuracy rate of 84.11%, and its training was much faster than that based on other machine learning algorithms.