Research on Traditional Chinese Medicine Constitution Classification Based on Machine Learning
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 cross-checking,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.
machine learningtraditional Chinese medicine constitutionfeature selectionclassification model
国家科技期刊平台
NSTL
万方数据
维普
