IMPROVING ACCURACY OF FUZZY RULE BASED MINING FOR HEART DISEASE DETECTION USING COST MATRIX
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Abstract
Improvement in healthcare system and improvements in the pharmaceutical domain are capable of curing multiple life threatening diseases now a days. Nevertheless, the appropriate time for detection and accuracy in the detection method is the major bottleneck for preventing loss of life. One of the major life threatening diseases is the coronary disease, where the heart is tend to malfunction and cause human death. Correct detection in early stages of the disease can prevent stopping of the heart function, thus saving of the patient life. A number research attempts are made to detect the heart disease. The demonstrations of the predictive analysis of the patient’s heart condition based on various body conditions are executed. Yet, the reports are biased with true negative results from those researchers impacting the wrong medication for the affected due to imperfect analysis. This ill medication is causing the side effects and effects of other severe diseases in the patient’s body. Hence, the demand of the modern research is to improve the accuracy of the predictive analysis. This work analyses the popular predictive determination using fuzzy rule based methods of heart disease and apply novel cost based matrix to improve the accuracy of detection.
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