Abstract
BackgroundCoronary heart disease (CHD) is a common cardiovascular disease that is extremely harmful to humans. In Traditional Chinese Medicine (TCM), the diagnosis and treatment of CHD have a long history and ample experience. However, the non-standard inquiry information influences the diagnosis and treatment in TCM to a certain extent. In this paper, we study the standardization of inquiry information in the diagnosis of CHD and design a diagnostic model to provide methodological reference for the construction of quantization diagnosis for syndromes of CHD. In the diagnosis of CHD in TCM, there could be several patterns of syndromes for one patient, while the conventional single label data mining techniques could only build one model at a time. Here a novel multi-label learning (MLL) technique is explored to solve this problem.MethodsStandardization scale on inquiry diagnosis for CHD in TCM is designed, and the inquiry diagnostic model is constructed based on collected data by the MLL techniques. In this study, one popular MLL algorithm, ML-kNN, is compared with other two MLL algorithms RankSVM and BPMLL as well as one commonly used single learning algorithm, k-nearest neighbour (kNN) algorithm. Furthermore the influence of symptom selection to the diagnostic model is investigated. After the symptoms are removed by their frequency from low to high; the diagnostic models are constructed on the remained symptom subsets.ResultsA total of 555 cases are collected for the modelling of inquiry diagnosis of CHD. The patients are diagnosed clinically by fusing inspection, pulse feeling, palpation and the standardized inquiry information. Models of six syndromes are constructed by ML-kNN, RankSVM, BPMLL and kNN, whose mean results of accuracy of diagnosis reach 77%, 71%, 75% and 74% respectively. After removing symptoms of low frequencies, the mean accuracy results of modelling by ML-kNN, RankSVM, BPMLL and kNN reach 78%, 73%, 75% and 76% when 52 symptoms are remained.ConclusionsThe novel MLL techniques facilitate building standardized inquiry models in CHD diagnosis and show a practical approach to solve the problem of labelling multi-syndromes simultaneously.
Highlights
Coronary heart disease (CHD) is a common cardiovascular disease that is extremely harmful to humans
Forecast results of syndrome models for inquiry diagnosis On the data set with all symptoms, taken k = 5, the models of ML-k-nearest neighbour (kNN) are built as described in the Methods Section
The mean accuracy obtained on the 6 syndrome labels is shown in Figure 1, and the results of kNN, RankSVM and BPMLL are shown in the Figure as a comparison, where the horizontal coordinate stands for the labels of syndromes forecasted and AP means the average results of the whole labels; the longitudinal coordinate stands for forecast accuracy with 100% as the highest value
Summary
Coronary heart disease (CHD) is a common cardiovascular disease that is extremely harmful to humans. We study the standardization of inquiry information in the diagnosis of CHD and design a diagnostic model to provide methodological reference for the construction of quantization diagnosis for syndromes of CHD. Multivariate statistics has some superiority in the solution of quantitative diagnosis in TCM, the problem on clinical data analysis with high nonlinearity could not be solved by these techniques. The complex interaction among different symptoms could not be reflected clearly, and the diagnostic rules of TCM could not be revealed comprehensively and widely. In this circumstance, non-linear data mining techniques are appealable in quantitative diagnosis, [8]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
