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Deep Neural Network Based Cardio Vascular Disease Prediction Using Binarized Butterfly Optimization

S. Amutha*, J. Raja Sekar

Department of Computer Science and Engineering, Mepco Schlenk Engineering College (Autonmous), Sivakasi, India

* Corresponding Author: S. Amutha. Email: email

Intelligent Automation & Soft Computing 2023, 36(2), 1863-1880.


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.


Cite This Article

S. Amutha and J. R. Sekar, "Deep neural network based cardio vascular disease prediction using binarized butterfly optimization," Intelligent Automation & Soft Computing, vol. 36, no.2, pp. 1863–1880, 2023.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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