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. Thi...

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Bibliographic Details
Published in2019 IEEE 9th International Conference on Electronics Information and Emergency Communication (ICEIEC) pp. 330 - 333
Main Authors Zhang, Yupu, Liu, Jinhai, Zhang, Zhihang, Huang, Junnan
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2019
Subjects
classification
Classification algorithms
Data models
decision tree
Decision trees
Feature extraction
machine learning
Machine learning algorithms
Predictive models
smoking behavior
Training
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Summary: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
ISBN:1728111897
9781728111896
ISSN:2377-844X
DOI:10.1109/ICEIEC.2019.8784698