Identification of Kidney-Yang Deficiency Syndrome in Osteoporosis Patients Based on Rule Ensemble Method of Bagging Combining LASSO Regression
10.13422/j.cnki.syfjx.20230449
- VernacularTitle:基于Bagging结合LASSO回归的规则集成方法对骨质疏松症患者肾阳虚证的辨识研究
- Author:
Feibiao XIE
1
;
Jing WANG
2
;
Xinghua XIANG
1
;
Wenyuan XU
1
;
Weiguo BAI
1
;
Mengyu LIU
1
;
Yaxin TIAN
1
;
Qianzi CHE
1
;
Yongjun WANG
2
;
Wei YANG
1
Author Information
1. Institute of Basic Research in Clinical Medicine，China Academy of Chinese Medical Sciences， Beijing 100700，China
2. Institute of Spine Disease，Shanghai University of Traditional Chinese Medicine（TCM），Shanghai 200032，China
- Publication Type:Journal Article
- Keywords:
osteoporosis;
kidney Yang deficiency syndrome;
rule ensemble;
prediction method;
identification model;
variable importance;
partial dependence
- From:
Chinese Journal of Experimental Traditional Medical Formulae
2023;29(23):150-157
- CountryChina
- Language:Chinese
-
Abstract:
ObjectiveTo investigate the identification of kidney Yang deficiency syndrome of patients with osteoporosis（OP）， and to form the clinical syndrome identification rules of traditional Chinese medicine（TCM）. MethodBasic information， etiology， clinical symptoms and other characteristics of 982 OP patients were included， and statistical tests were used to screen the variables associated with kidney Yang deficiency syndrome. Taking the decision tree as the base model， bootstrap aggregation algorithm（Bagging algorithm） was utilized to establish the classification model of kidney Yang deficiency syndrome in OP， generating numerous rules and removing redundancy. Combining least absolute shrinkage and selection operator（LASSO） regression to screen key rules and integrate them to construct an identification model， achieving the identification of kidney Yang deficiency syndrome in OP patients. ResultEighteen key identification rules were screened out， and of these， where 11 rules with regression coefficients>0 correlated positively with the kidney Yang deficiency syndrome， the rule with the highest coefficient was chilliness（present）&feverish sensation over the palm and sole（absent）. The other 7 rules with regression coefficients<0 correlated negatively with the syndrome， the rule with the lowest coefficient was reddish tongue（present）&diarrhea（absent）&deficiency of endowment（absent）. According to the regression coefficients of each key rule， variables with importance>0.2 were ranked as chilliness， reddish tongue， feverish sensation over the palm and sole， cold limbs， clear urine， diarrhea， deficiency of endowment， prolonged illness. The results of the partial dependence analysis of the identification model showed that compared to OP patients without chilliness， those with chilliness（present） had a 0.266 8 higher probability of being identified as having kidney Yang deficiency syndrome， indicating that this variable had the highest impact on identification of the syndrome. Similarly， compared to OP patients without reddish tongue， those with reddish tongue had a 0.141 9 lower probability of being identified as having kidney Yang deficiency syndrome， indicating that this variable had the highest impact on identifying non-kidney Yang deficiency syndrome. The accuracy， sensitivity， specificity and area under receiver operating characteristic curve（AUC） of the established kidney Yang deficiency syndrome identification model in the test set were 0.865 9， 0.853 7， 0.872 0 and 0.931 5， respectively. ConclusionA precise identification model of OP kidney Yang deficiency syndrome is conducted basing on the rule ensemble method of Bagging combining LASSO regression， and the screened key rules can explain the identification process of kidney Yang deficiency syndrome. In this research， according to the regression coefficients of rules， the importance and partial dependence of variables， combined with the thinking of TCM， the influence of patient characteristics on the identification of syndromes is described， so as to reveal the primary and secondary syndromes of identification and assist the clinical identification of kidney Yang deficiency syndrome.
