基于多标签机器学习的糖尿病肾病中医“同病异证”风险评估模型的构建

Risk assessment model of diabetic nephropathy with"same disease and different syndromes"in traditional Chinese medicine based on multi-label machine learning

目的基于多标签机器学习算法策略,构建符合中医特色的糖尿病肾病“同病异证”风险评估模型并比较其效能,为辅助中医药防治糖尿病肾病提供更高效的方法。方法利用8795条糖尿病肾病诊疗数据,基于复杂网络社区发现算法进行特征选择,分别在“转化问题”与“算法适应”2种算法策略下,使用支持向量机(SVM)、自组织增强(AdaBoost)、多标签条件随机场(ML-RBF)、多标签最近邻(ML-KNN)等算法构建多标签学习模型,并使用5种评价指标对模型效能进行比较。结果最终构建了具有8795条样本,113个指标、15个证型标签的糖尿病肾病多标签数据集。模型评价方面,ML-KNN在海明损失(Hamming Loss)、排序损失(ranking Loss)、覆盖度(Coverage)指标上性能最好;SVM在1-错误率(one-error)指标上出现3次最小值,但仍以KNN的one-error指标平均值最佳;4种模型的平均精度(average precision)均在90%以上,以ML-KNN及ML-RBF性能相对最佳。上述4种模型在糖尿病肾病“同病异证”的多证型风险评估方面均具有较好的诊断效能,综合来看ML-KNN性能相对最优。结论多标签机器学习算法能够用于中医多证型等复杂情况的风险评估,为辅助中医药防治糖尿病肾病提供参考,也为多标签机器学习在全科医学临床多病种诊疗的应用提供方法学借鉴。

Objective To construct a risk assessment model of diabetic nephropathy with"same disease and different syndromes"in traditional Chinese medicine based on the multi-label machine learning algorithm and compare its effectiveness,and to provides an efficient way to assist traditional Chinese medicine in preventing and treating diabetic nephropathy.Methods Based on the data of 8795 diabetic nephropathy,feature selection was carried out based on the complex network community detection algorithm.Under the two algorithms of"transformation problem"and"algorithm adaptation",the SVM,AdaBoost,ML-RBF and ML-KNN algorithms were used to construct the multi-label learning model,and five evaluation indexes were used to compare the model efficiency.Results A multi-label dataset of diabetic nephropathy with 8795 samples,113 characteristics and 15 syndrome types was constructed.In terms of model evaluation,ML-KNN had the best performance in Hamming loss,ranking loss and coverage indicators;SVM had three minimum values on one error index,but the average value of one error index of KNN was still the best.The average precision of the four models was more than 90%,and the performance of ML-KNN and ML-RBF were relatively the best.The above four models had better diagnostic efficiency in the multiple syndrome risk assessment of diabetic nephropathy with"same disease and different syndromes",and ML-KNN performance was relatively optimal.Conclusion The multi-label machine learning algorithm can be applied to the risk assessment of complex syndromes,such as TCM.It provides a reference for assisting Chinese medicine in the prevention and treatment of diabetic nephropathy and provides a methodological reference for the application of multi-label machine learning in clinical multi-disease diagnosis and treatment in general practice.