Abstract
The main objective of this paper is to predict the possibility of heart diseases at its early stages with less number of attributes. Pre-processing helps to improve sensor images by removing noise present in it. In the proposed work, a coupled modified diffusivity function is applied in Laplacian pyramid domain of an image, to eliminate noise and retains subtle features simultaneously. Our approach integrates anthropometric data and physiological data of heart diseases by proposing hybrid feature selection method for prediction of heart diseases using soft computing techniques. We ran experiments in neural network and SVM and proved that the neural network predicts 92% of accuracy and SVM predicts 97% of accuracy. The results show the proposed approach leads to a superior feature selection process in terms of sinking the number of variable required and an increased in classification accuracy for better prediction.
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