An Ensemble Feature Extraction and Deep Learning Approach for Prediction of Heart Disease

Abstract

Cardiovascular disease is commonly known as heart disease. Heart disease prediction in early stages
is more complex to get the accurate results. Data mining (DM), Machine Learning (ML) and Deep Learning
(DL) many domains doing huge research on medical data especially in heart disease prediction. Heart is most
important part in human body. Heart attack is one of the heart diseases. Deep Learning (DL) plays the major
role in prediction of heart disease. In this paper, An Ensemble Feature Extraction Learning Approach
(EFEDLA) is introduced for the early prediction of heart diseases with the patient data. The dataset is collected
from Kaggle website. To overcome the difficulties in dataset the proposed system utilized the enhanced preprocessing technique. The proposed system follows the few steps to predict the heart disease in the early stages.
The performance of proposed system is compared with several existing systems.