Feature Selection and Dwarf Mongoose Optimization Enabled Deep Learning for Heart Disease Detection

Heart disease causes major death across the entire globe. Hence, heart disease prediction is a vital part of medical data analysis. Recently, various data mining and machine learning practices have been utilized to detect heart disease. However, these techniques are inadequate for effectual heart di...

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Vydané v:Computational intelligence and neuroscience Ročník 2022; s. 1 - 11
Hlavní autori: Balasubramaniam, S., Satheesh Kumar, K., Kavitha, V., Prasanth, A., Sivakumar, T. A.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States Hindawi 07.12.2022
John Wiley & Sons, Inc
Predmet:
Accuracy
Algorithms
Analysis
Animals
Cardiovascular disease
Data analysis
Data Mining
Data processing
Datasets
Deep Learning
Feature selection
Heart
Heart diseases
Heart Diseases - diagnosis
Helogale parvula
Herpestidae
Humans
Information management
Literature reviews
Machine Learning
Mathematical optimization
Medical advice systems
Medical diagnosis
Medical research
Medicine, Experimental
Methods
Missing data
Neural networks
Optimization
Ohio
ISSN:1687-5265, 1687-5273, 1687-5273
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Popis
Shrnutí:Heart disease causes major death across the entire globe. Hence, heart disease prediction is a vital part of medical data analysis. Recently, various data mining and machine learning practices have been utilized to detect heart disease. However, these techniques are inadequate for effectual heart disease prediction due to the deficient test data. In order to progress the efficacy of detection performance, this research introduces the hybrid feature selection method for selecting the best features. Moreover, the missed value from the input data is filled with the quantile normalization and missing data imputation method. In addition, the best features relevant to disease detection are selected through the proposed hybrid Congruence coefficient Kumar–Hassebrook similarity. In addition, heart disease is predicted using SqueezeNet, which is tuned by the dwarf mongoose optimization algorithm (DMOA) that adapts the feeding aspects of dwarf mongoose. Moreover, the experimental result reveals that the DMOA-SqueezeNet method attained a maximum accuracy of 0.925, sensitivity of 0.926, and specificity of 0.918.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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Academic Editor: Navid Razmjooy
ISSN:1687-5265
1687-5273
1687-5273
DOI:10.1155/2022/2819378