Kushner-Stratonovich Dice Segmented Curvelet (KSDSC) deep convolutional neural learning for heart disease prediction

Abstract

Data mining is commonly used method for processing large amount of data in heart disease prediction. Many heart disease prediction researches are carried out by different authors. But the accuracy level was not improved. In order to address these issues, Kushner-Stratonovich Dice Segmented Haar Wavelet (KSDSC) Deep Convolutional Neural Learning Model is introduced. The main aim of KSDSC Model is to perform efficient heart disease prediction with five layers, namely one input layer, three hidden layers and one output layer. Initially, ultrasound images are collected as an input at input layer. An image pre-processing is carried out using Kushner-Stratonovich Filter to eliminate the noisy pixels from US image and transmitted to hidden layer 2. Sørensen–Dice Image Segmentation Process partitions the preprocessed image into number of divisions in hidden layer 2. After that in hidden layer 3, the multiple features are extracted using Curvelet transform from segmented image and sent to the output layer. The output layer uses softmax yolov4 darknet53 activation function to match extracted features for heart disease prediction. Experimental analysis is performed on parameters such as prediction accuracy, false positive rate, and prediction time with respect to a number of US images.