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Analyzing Health Seeking Behavior of Chinese Residents and Their Influencing Factors Based on CHNS Data
Author(s) -
JiaYu Xue,
Xinyue Ren,
Yuanjie Xu,
Qianqian Feng
Publication year - 2019
Publication title -
procedia computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.334
H-Index - 76
ISSN - 1877-0509
DOI - 10.1016/j.procs.2019.12.057
Subject(s) - support vector machine , computer science , logistic regression , artificial intelligence , machine learning , big data , hotspot (geology) , data mining , geophysics , geology
With the improvement of people’s living standards, Chinese residents have paid more and more attention to health. The development of science and technology has made medical health big data emerge and has gradually become a research hotspot. In our paper, we first adopted Logistic Regression to find out the important variables for health seeking behavior and then we verified that this behavior can be predicted based on machine learning algorithms. By comparing classic Support Vector Machine (SVM) algorithm and improved SVM model with different variables, we finally proposed a SVM model based on SMOTE algorithm that is relatively optimal for predicting health seeking behavior.

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