Early Prediction of Heart Conditions by K-Means Consensus Clustering and Convolution Neural Network

Abstract

Today, predicting heart disease is the most challenging task in the medical world.In this generation, due to complications of heart disease, a person dies each minute or so.Data science plays a significant role in collecting large amounts of data in the medical domain.Since the prediction of heart disease is a complex task, it is necessary to anticipate earlier to avoid risks and inform the patient in advance.Machine learning, data mining, and deep learning are the three methods that provide the methodology and technology to leverage the relevant knowledge to make the right decision.Heart and vascular diseases are associated with cardiovascular diseases including Heart Disease (HD).Extensive research into heart disease has been carried out using vast amounts of data on all other aspects of HD.This paper, which is a cardiac dataset, comes out of the available public health dataset.In this article, we proposed a new Conventional Neural Network (CNN) architecture that was used to extract and categorize histopathological images using the K-means Consensus Clustering.The use of the deep neural network, the selection, and recovery of functionalities is the important process carried out before the classification of the dataset.In consequence, the dataset is pre-formed by the training process.The proposed model is associated with temporal data modeling use the earlier CNN prediction of HD.We achieved good results with the cardiac dataset compared to the existing results.The outcome of the proposed work achieves a precision rate of 97%.