A hybrid rule mining approach for cardiovascular disease detection in traditional Chinese medicine

Abstract

According to the reports of the World Health Organization, the abnormal total cholesterol is one of the major risk factors for cardiovascular diseases. Supporting this, a number of prospective studies have examined that the risk of cardiac morbidity and mortality is directly associated with the concentration of plasma total cholesterol. Traditional Chinese medicine has been used to treat cardiovascular diseases since it creates milder healing effects and incurs fewer side effects than Western approaches. However, in real world, the traditional Chinese medicine diagnosis using the meridian system is highly complicated in nature so that it is difficult to create a general model. In our research, a hybrid evolutionary rule mining approach is proposed to assess the total cholesterol data patterns detected from the traditional Chinese medicine meridian energy. Based on the proposed approach, a rule-based decision-making model can be developed with maximum classification accuracy. Through a numerical experiment, the results of this study were compared with the commercial data mining software and the proposed approach is shown to be a promising method for improving prediction accuracy with fewer type II errors. The research outcomes may have benefits to help diagnose and treat cholesterol abnormalities in traditional Chinese medicine clinical therapy.

Keywords

Rule mining traditional Chinese medicine total cholesterol decision-making

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