Study on the Diagnosis Model of Phlegm-Dampness Obstruction Syndrome in Patients with Stable Angina Pectoris Due to Coronary Heart Disease Based on Machine Learning
10.19879/j.cnki.1005-5304.202407108
- VernacularTitle:基于机器学习的冠心病稳定型心绞痛痰浊闭阻证诊断模型研究
- Author:
Haoran CHEN
1
;
Tong JIANG
;
Yi ZHENG
;
Weiwei WANG
;
Ying LIU
;
Kejun WANG
Author Information
1. 滨州医学院中医学院,山东 烟台 264000
- Keywords:
coronary heart disease;
machine learning;
clinical prediction model;
syndrome differentiation and treatment;
objective four diagnostic methods
- From:
Chinese Journal of Information on Traditional Chinese Medicine
2024;31(12):142-150
- CountryChina
- Language:Chinese
-
Abstract:
Objective To construct a diagnostic model for the phlegm-dampness obstruction syndrome in patients with coronary heart disease stable angina pectoris(CSAP);To provide a reference for clinical syndrome differentiation.Methods Totally 305 patients'clinical data were collected from the Department of Cardiology,Dongying Hospital Affiliated to Shandong University of Traditional Chinese Medicine,from May 2022 to January 2024.The least absolute shrinkage and selection operator(LASSO)was used to select features,and multiple models were constructed and compared using machine learning(ML)algorithms.The optimal ML model was selected for training,validation,and testing.Finally,the operational logic of the optimal model was explained using Shapley Additive Explanations(SHAP),and two typical examples were provided to help users understand the model's operational logic.Results LASSO regression identified chest pain,body mass index(BMI),limb heaviness,drinking history,age,triglycerides(TG),total cholesterol(TC),and low-density lipoprotein cholesterol(LDL-C)as features included in the model.After comparing multiple models,the Gaussian Naive Bayes(GNB)model demonstrated the best performance.The final constructed GNB model achieved an average AUC of 0.938(95%CI:0.903-0.972)in the training set,an average AUC of 0.927(95%CI:0.851-0.992)in the validation set,and an AUC of 0.856(95%CI:0.751-0.961)in the test set.The learning curve showed that the error between the training and validation sets in the model converged as the number of training samples increased.The calibration curve showed that the model had good consistency in predicting the probability of observed phlegm-dampness obstruction syndrome patients.The clinical decision curve(DCA)showed that the model could provide clinical benefits for patients at a decision threshold below 0.7.The features ranked by SHAP importance in order were chest pain,BMI,LDL-C,TG,limb heaviness,TC,drinking history and age.Conclusion The diagnostic model for CSAP phlegm-dampness obstruction syndrome constructed in this study can assist physicians in the syndrome differentiation of patients,thereby enabling the formulation of integrated clinical treatment plans combining traditional Chinese and Western medicine,and aiding patients in achieving better clinical therapeutic outcomes.
