Asian Science Citation Index is committed to provide an authoritative, trusted and significant information by the coverage of the most important and influential journals to meet the needs of the global scientific community.  
ASCI Database
308-Lasani Town,
Sargodha Road,
Faisalabad, Pakistan
Fax: +92-41-8815544
Contact Via Web
Suggest a Journal
Asian Journal of Information Technology
Year: 2017  |  Volume: 16  |  Issue: 7  |  Page No.: 639 - 644

A Hybrid Heart Disease Prediction System using Evolutionary Learning Algorithms

S. Mohandoss, V. Sai Shanmuga Raja and S.P. Rajagopalan    

Abstract: Cardiovascular illness remains the greatest reason for deaths worldwide and the heart disease prediction at the early stage is significance. In this study, we propose a hybrid heart disease prediction system using evolutionary learning algorithms like cascaded neural network and Genetic algorithm. It is used for heart disease prediction at the early stage utilizing the patient’s therapeutic record. The results are compared with the known supervised classifier Support Vector Machine (SVM). During classification, 13 attributes are given as input to the CNN classifier to predict the risk of heart illness. The proposed framework can be used as a guide by the doctors to predict the disease in a more productive way. The effectiveness of the classifier is tried utilizing the records gathered from 270 patients. The outcomes demonstrate that the Genetic based CNN classifier can anticipate the probability of patients with coronary illness in a more effective manner.

Fulltext    |   Related Articles   |   Back
  Related Articles

Copyright   |   Desclaimer   |    Privacy Policy   |   Browsers   |   Accessibility