%0 Journal Article
%T Inquiry diagnosis of coronary heart disease in Chinese medicine based on symptom-syndrome interactions
%A Guo-Zheng Li
%A Sheng Sun
%A Mingyu You
%A Ya-Lei Wang
%A Guo-Ping Liu
%J Chinese Medicine
%D 2012
%I BioMed Central
%R 10.1186/1749-8546-7-9
%X Relative associated density (RAD) was used to analyze the one-way links between the symptoms or syndromes or both. RAD results were further used in symptom selection.Analysis of a dataset of clinical CHD diagnosis revealed some significant relationships, not only between syndromes but also between symptoms and syndromes. Using RAD to select symptoms based on different classifiers improved the accuracy of syndrome prediction. Compared with other traditional symptom selection methods, RAD provided a higher interpretability of the CM data.The RAD method is effective for CM clinical data analysis, particular for analysis of relationships between symptoms in diagnosis and generation of compact and comprehensible symptom feature subsets.Western medicine classifies coronary heart disease (CHD) as a kind of myocardial dysfunction and organic lesion, occasionally accompanied by coronary artery stenosis and vertebrobasilar insufficiency [1]. In contrast, Chinese medicine (CM) classifies CHD as a type of chest paralysis and heart pain, for which effective diagnosis and treatment are available [2].CM treatment is based primarily on syndrome differentiation and physiology and pathology of Zang-fu organs and meridians. In CM, a symptom represents an observable indicator of abnormality, while a syndrome is the disease state manifested by symptoms. The connections between symptoms and syndromes in CM are not clearly defined. Therefore, it is necessary to delineate different relationships between symptoms and syndromes and explain the diagnosis results in comprehensible terms [3].Machine learning builds empirical models on data for analysis and forecasting, which has recently been used for CM data analysis. Huang and Gao [4] reviewed several classifiers of data mining in CM. Li and Huang [5] used fuzzy neural network for analysis of CM ingredients. Wang et al. [6] used a decision tree method to generate prediction models for CM hepatitis data and liver cirrhosis data. Zhang et al. [
%U http://www.cmjournal.org/content/7/1/9