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

Identification of Kidney-Yang Deficiency Syndrome in Osteoporosis Patients Based on Rule Ensemble Method of Bagging Combining LASSO Regression

目的:研究骨质疏松症(OP)患者的肾阳虚证辨识,形成中医临床辨证规则。方法:纳入982例OP患者的基本信息、病因病机、临床症状等特征信息,通过统计检验筛选数据中与肾阳虚证相关的变量。以决策树为基础模型,应用引导聚集算法(Bagging算法)建立OP肾阳虚证分类模型,生成大量规则并去除冗余;结合最小绝对值收敛和选择算子(LASSO)回归筛选关键规则并集成规则建立辨识模型,实现对OP患者肾阳虚证的辨识。结果:筛选出了18条关键辨识规则,其中11条规则的回归系数>0,对辨识为肾阳虚证具有正向影响,系数最高的规则为畏寒(“有”)&手足心热(“无”);7条规则的回归系数<0,对辨识为肾阳虚证具有负向影响,系数最低的规则为舌红(“有”)&大便溏(“无”)&禀赋不足(“无”)。根据各关键规则的回归系数计算得到重要性>0.2的变量依次为畏寒、舌红、手足心热、肢冷、小便清、大便溏、禀赋不足、久病。辨识模型的偏依赖性分析结果显示,畏寒取值为“有”相较取值为“无”的OP患者,被辨识为肾阳虚证的概率高0.2668,该变量对辨识为肾阳虚证具有最高影响。舌红取值为“有”相较取值为“无”的OP患者,被辨识为肾阳虚证的概率低0.1419,该变量对辨识为非肾阳虚证具有最高影响。所建立的OP肾阳虚证辨识模型在测试集的正确率、灵敏度、特异度、受试者工作特征曲线(ROC曲线)下面积(AUC)分别为0.8659、0.8537、0.8720、0.9315。结论:基于Bagging结合LASSO回归的规则集成方法,构建了更加精确的OP肾阳虚证辨识模型,筛选出的关键规则能较好解释肾阳虚证辨识过程,可辅助OP肾阳虚证的中医临床辨证。

Objective:To 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).Method:Basic 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.Result:Eighteen 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 0.2668 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 0.1419 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 0.8659,0.8537,0.8720 and 0.9315,respectively.Conclusion:A 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.