Efficient heart disease prediction-based on optimal feature selection using DFCSS and classification by improved Elman-SFO

Prediction of cardiovascular disease (CVD) is a critical challenge in the area of clinical data analysis. In this study, an efficient heart disease prediction is developed based on optimal feature selection. Initially, the data pre-processing process is performed using data cleaning, data transforma...

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Vydáno v:	IET systems biology Ročník 14; číslo 6; s. 380 - 390
Hlavní autoři:	Wankhede, Jaishri, Kumar, Magesh, Sambandam, Palaniappan
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	England The Institution of Engineering and Technology 01.12.2020
Témata:	Algorithms Bayes methods California Irvine Cleveland heart disease dataset cardiology cardiovascular disease cardiovascular system clinical data analysis Computer Simulation CVD dataset data analysis data classification data cleaning data handling data mining data normalisation data pre‐processing process data transformation decision function‐based chaotic salp swarm algorithm decision trees DFCSS–IENN‐based SFO algorithm DFCSS–IESFO diseases efficient heart disease prediction‐based feature extraction feature selection feature selection process Heart Diseases - classification high‐classification accuracy Humans improved Elman neural network improved Elman‐SFO learning (artificial intelligence) medical diagnostic computing neural nets Neural Networks, Computer optimal feature selection optimal features optimal weight value pattern classification regression analysis Research Article sailfish optimisation algorithm support vector machines values imputation data mining regression analysis California Irvine Cleveland heart disease dataset feature extraction efficient heart disease prediction-based data classification CVD dataset clinical data analysis DFCSS–IENN-based SFO algorithm data transformation learning (artificial intelligence) feature selection optimal weight value medical diagnostic computing improved Elman neural network pattern classification cardiovascular disease cardiovascular system support vector machines data analysis optimal features DFCSS–IESFO diseases data normalisation data pre-processing process cardiology high-classification accuracy feature selection process improved Elman-SFO data cleaning decision trees sailfish optimisation algorithm decision function-based chaotic salp swarm algorithm Bayes methods data handling optimal feature selection values imputation neural nets
ISSN:	1751-8849, 1751-8857, 1751-8857
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