TY  - JOUR
T1  - A Hybrid Heart Disease Prediction System using Evolutionary Learning Algorithms
AU - Mohandoss, S. AU - Raja, V. Sai Shanmuga AU - Rajagopalan, S.P. 
JO  - Asian Journal of Information Technology
VL  - 16
IS  - 7
SP  - 639
EP  - 644
PY  - 2017
DA  - 2001/08/19
SN  - 1682-3915
DO  - ajit.2017.639.644
UR  - https://makhillpublications.co/view-article.php?doi=ajit.2017.639.644
KW  - Prediction
KW  -anticipate
KW  -SVM
KW  -algorithms
KW  -significance
KW  -attributes
AB  - 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.
ER  -