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

作　　者：S.Amutha J.Raja Sekar 

机构地区：[1]Department of Computer Science and Engineering,Mepco Schlenk Engineering College(Autonmous),Sivakasi,India

出　　处：《Intelligent Automation & Soft Computing》2023年第5期1863-1880,共18页智能自动化与软计算（英文）

摘　　要：In this digital era,Cardio Vascular Disease(CVD)has become the lead-ing 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 pro-pose 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 accu-racy with minimal complexity.The proposed system is tested with two datasets namely the Heart disease dataset from UCI repository and CVD dataset from Kag-gle 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 dis-ease data set from UCI repository yielding an accuracy of 80.98%for Kaggle repository for cardiovascular disease dataset.

关 键 词：Deep neural network cardio vascular disease binarized butterfly optimization algorithm feature selection 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] R54[自动化与计算机技术—控制科学与工程]