@Article{iasc.2023.028903,
AUTHOR = {S. Amutha, J. Raja Sekar},
TITLE = {Deep Neural Network Based Cardio Vascular Disease Prediction Using Binarized Butterfly Optimization},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {36},
YEAR = {2023},
NUMBER = {2},
PAGES = {1863--1880},
URL = {http://www.techscience.com/iasc/v36n2/50850},
ISSN = {2326-005X},
ABSTRACT = {In this digital era, Cardio Vascular Disease (CVD) has become the leading cause of death which has led to the mortality of 17.9 million lives each year.
Earlier Diagnosis of the people who are at higher risk of CVDs helps them to
receive proper treatment and helps prevent deaths. It becomes inevitable to propose a solution to predict the CVD with high accuracy. A system for predicting
Cardio Vascular Disease using Deep Neural Network with Binarized Butterfly
Optimization Algorithm (DNN–BBoA) is proposed. The BBoA is incorporated
to select the best features. The optimal features are fed to the deep neural network
classifier and it improves prediction accuracy and reduces the time complexity.
The usage of a deep neural network further helps to improve the prediction accuracy with minimal complexity. The proposed system is tested with two datasets
namely the Heart disease dataset from UCI repository and CVD dataset from Kaggle Repository. The proposed work is compared with different machine learning
classifiers such as Support Vector Machine, Random Forest, and Decision Tree
Classifier. The accuracy of the proposed DNN–BBoA is 99.35% for the heart disease data set from UCI repository yielding an accuracy of 80.98% for Kaggle
repository for cardiovascular disease dataset.},
DOI = {10.32604/iasc.2023.028903}
}