Research on Traditional Chinese Medicine Constitution Classification Based on Machine Learning
PAN Kangning1a, WANG Hongjie1a, YU Xia1b, SUN Wanchen2
1. a. Department of Medical Equipment; b. Department of Ultrasound II, Weihai Maternal and Child Health Hospital,
Weihai Shandong 264200, China; 2. Medical Department, Weihai Chest Hospital, Weihai Shandong 264200, China
Abstract：Objective To screen out the optimal feature subset and construct a gradient boosting decision tree (GBDT) model to
classify peaceful constitution and biased constitution by using the filter-type feature selection method of random forest. Methods A
total of 2756 subjects were selected as the research objects, and a cross-sectional survey was used to conduct a questionnaire survey.
The signals of twenty-four original points on twelve meridians and basic information including height, weight, age and gender were
collected and constructed as database. After the data set was preprocessed, a random forest feature selection method was used to
filter the optimal subset of features, and then GBDT algorithm was used to construct a machine learning based pacific-biased body
binary classification. And the calculation accuracy, precision, recall and F1 score were comprehensively evaluated by ten fold crosschecking,
and the performance of the model was evaluated comprehensively. Results Twenty-two features were filtered to form the
optimal feature subset, and the accuracy, precision, recall, and F1 scores of the GBDT model constructed using the filtered feature
subset were 92.86%, 93.65%, 93.08% and 0.92, respectively. Conclusion The random forest feature selection method can help to
filter the optimal feature subset, and the GBDT can provide help for traditional chinese medicine body classification studies.
潘康宁1a，王洪杰1a，于霞1b，孙万晨2. 基于机器学习的中医体质分类研究[J]. 中国医疗设备, 2024, 39(1): 6-11.
PAN Kangning1a, WANG Hongjie1a, YU Xia1b, SUN Wanchen2. Research on Traditional Chinese Medicine Constitution Classification Based on Machine Learning. China Medical Devices, 2024, 39(1): 6-11.
