An Effectual Supervised Learning Based Automatic Classification Of Coronary Heart Disease
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Abstract
Most of the deaths are in the world are due to heart diseases which should be controlledefficiently. Among all heart diseases, coronary artery disease is very common and dangerous worldwide. These diseases are not easily identifiable and need extra care and precision in the health monitoring of the patient. In this paper, an effectual classification and optimization process is developed which puts light on the machine learning approach to identify coronary artery disease. The simulation is taken place in the MATLAB environment on which the 303 patient’s data is analyzed in terms of the characteristics which are helpful in the diagnosis of coronary heart disease. In the proposed approach the feature engineering is used in which the feature extraction and instance selection are evaluated and the system training is performed to achieve high accuracy and low error rates. Also, the over-fitting problem is resolved using the proposed approach which helps to achieve high sensitivity, specificity, and recognition rate with low false acceptance and rejection rates.
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