Heart Disease Classification And Risk Prediction By Using Convolutional Neural Network

Document Type : Primary Research paper


1 Assistant Professor, Department of Computer Science and Engineering, Keshav Memorial Institute of Technology, Hyderabad, Telangana, India.

2 Professor, Department of Computer Science and Engineering, CVR College of Engineering,Rangareddy, Telangana, India.


one of the biggest causes of death in today's globe is heart disease. Heart disease is the most common cause of death for both men and women.This has a significant impact on human life. The majority of the time, heart disease diagnosis is based on a complex mixture of clinical and pathological evidence. Machine learning effectively aids in making decisions and predictions from the massive amount of data generated by the healthcare industry, which pays to more noise, such as missing data, duplicate data, etc. The efficiency of classification and the accuracy of disease prediction are also affected by duplicate and null values. Various traditional machine learning algorithms have been implemented to increase heart disease prediction performance in the current method. Our proposed work includes Machine Learning-based classifiers to assess classification accuracy and the use of Deep Learning techniques such as Convolutional Neural Network Unidirectional Risk Prediction (CNN-UDRP) to enhance the accuracy of heart disease prediction. We also compare the classification accuracy of the KNN, SVM, and Naive Bayes Classifiers on many healthcare datasets.