EXPERT SYSTEM FOR CORONARY HEART DISEASE - BUILT USING ARTIFICIAL INTELLIGENCE?Journal: International Journal of Computer Science and Mobile Computing - IJCSMC (Vol.3, No. 5)
Publication Date: 2014-05-30
Authors : Suchithra; P.Uma Maheswari;
Page : 583-598
Keywords : Artificial Intelligence Techniques; Clinical Decision Support System (CDSS); Coronary Heart Disease;
Coronary Heart Disease is a disease which is difficult to diagnose and is vey commonly identified only during the mortality of an individual. The World Health Organization (WHO) reported that 70 per cent deaths occur in subjects less than 70 years of age in India and in other developing countries. Since Coronary Heart Disease (CHD) is becoming an epidemic in India, there is a terrific need for effective solution for risk identification as earlier as possible. Despite ccomputerized clinical guidelines may provide benefits for health outcomes and costs, however, their successful implementation are more challenging to investigate significant problems. One effective solution is to achieve an optimal trade-off between data ambiguity and good decision-making which would further help in the integration of data mining and artificial intelligence techniques. In this work, a novel approach is proposed to develop a clinical decision support system (CDSS) for heart disease diagnosis using data mining and Artificial Intelligence techniques.The major goal of this paper is to build an expert system for diagnosing the presence of Ischemic Heart Disease with an integrated automated classifier using Artificial Intelligence techniques. A retrospective data set that included 1000 clinical cases is taken for the work. 80 sets were discarded during preprocessing. Tests were run on 920 cases using weka classifiers  available in weka 3.7.0. The proposed algorithm formalizes the treatment of vagueness in decision support architecture which also evaluates the performance measures among them. Experimental results demonstrate the effectiveness of the proposed CDSS in heart disease diagnosis.
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