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Intelligent categorization method for diagnosing cardiovascular diseases hierarchically

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The effectiveness and adaptability of such method is demonstrated and tested by applying it in diagnosing 5 most common and important CVDs successfully. | Journal of Automation and Control Engineering Vol. 3, No. 6, December 2015 Intelligent Categorization Method for Diagnosing Cardiovascular Diseases Hierarchically Mubo Chen and Mingchui Dong Dept. of ECE, FST, University of Macau, Macau S. A. R., China Email: chenmubocs@gmail.com parameters (HDPs) are derived by using the model of elastic cavity, which are capable of revealing cardiovascular health status and variation tendency [9]. Besides, in medical theory, a symptom is expression of the presence of disease or abnormality. Hence, hard efforts of exploring HDPs, symptom parameters (SPs) as well as physiological parameters (PPs) have been made for CVDs detection by applying machine learning technology [10]-[13]. There exist many models for exploring HDPs, SPs and PPs, but the most important distinction is: does the model put the parameters into a classifier all at once, or does the model put the parameters hierarchically. For the former case, Ref. [11] combines association analysis and information gain feature selection for SPs on multi-syndrome data of CHD considering the association of SPs; Ref. [12] proposes a hybrid optimization based on multi-label feature selection to effectively reduce the data dimension and improves the classification performance on CHD; Ref. [13] shows high accuracy in detecting CVDs by using SVM (support vector machine) based on HDPs and PPs, etc. However, the aforementioned methods may contradict to the doctors’ clinical diagnosis procedure. In practice, doctor normally ranks all parameters and selects specified ones with most pertinence to diagnose diseases. If it fails, doctor would turn to the less pertinent parameters. Such a manner makes the clinical reasoning procedure representing “hierarchically” character. For this reason, detecting CVDs hierarchically by using machine learning has attracts more and more attention. However, the hierarchical mode inevitably leads to a bottleneck problem: How to divide HDPs, SPs& PPsinto .