Acta Scientific Computer Sciences

Research Article Volume 5 Issue 5

Heart Disease Prediction System Using Convolutional Neural Networks

V Krishnaiah*

Associate Professor, Department of Computer Science And Engineering, Neil Gogte Institute of Technology, Kachawani Singaram Village, Hyderabad, TS, India

*Corresponding Author: V Krishnaiah, Associate Professor, Department of Computer Science And Engineering, Neil Gogte Institute of Technology, Kachawani Singaram Village, Hyderabad, TS, India.

Received: March 08, 2023; Published: April 13, 2023


Now a days, based on different reasons heart diseases are increasing rapidly. If we find out or identify the heart diseases in human beings at an early stage, it is easy to prevent the disease and help the patients. Even though cardiologists and health centers gather relevant data and information every day, but, not applying the knowledge of machine learning algorithms to retrieve valuable of prediction. The main objective of this research is to predict and classify heart diseases by using proposed convolutional neural network classifier. In this classification of evaluation process, feed forward process and back propagation methods will be applied in between the hidden layers. Due to this, the proposed CNN classifier gives best accuracy. By applying this trained classifier has identified the given data, which are either normal or abnormal. So, the entire research has been implemented in Python which produced good results.

Keywords: Deep Learning; Classification; Convolutional Neural Network; Heart Disease


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Citation: V Krishnaiah. “Heart Disease Prediction System Using Convolutional Neural Networks". Acta Scientific Computer Sciences 5.5 (2023): 64-72.


Copyright: © 2023 V Krishnaiah. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


Acceptance rate35%
Acceptance to publication20-30 days

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