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FA-XGBoost model and precision medical prediction
GONG Yicheng 1 *,YU Li 2,ZHANG Yanna 2
1.Hubei Province Key Laboratory of Systems Science in Metallurgical Process, Wuhan University of Science and Technology, Wuhan　430065
2.Department of Mathematics and Statistics, Science College, Wuhan University of Science and Technology ,Wuhan　430065
*Correspondence author
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Funding: none
Opened online:25 February 2019
Accepted by: none
Citation: GONG Yicheng,YU Li,ZHANG Yanna.FA-XGBoost model and precision medical prediction[OL]. [25 February 2019] http://en.paper.edu.cn/en_releasepaper/content/4747304
 
 
To predict scientifically and effectively on the increasing scale and dimension data with lack of some feature values, this paper proposes an XGBoost coupled with factor analysis model (FA-XGBoost), where factor analysis (FA) is used to reduce dimension of feature variables and then an XGBoost model is trained by using the data after FA. To test the model\'s effect, this paper analyzes some medical data, which are provided by the Tianchi Precision Medical Contest. The mean-square error (MSE) and the running time (t) are respectively 1.3800 and 1.3771 seconds for FA-XGBoost. Finally, we compared the FA-XGBoost model with four models based on decision trees. In general, GBDT and FA-XGBoost performed best on MSE, while FA-XGBoost worked best on running time.
Keywords:Statistics; XGBoost; factor analysis; blood glucose prediction; diabete
 
 
 

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