Nature Inspired Optimization with Hybrid Machine Learning Model for Cardiovascular Disease Detection and Classification

Nature Inspired Optimization with Hybrid Machine Learning Model for Cardiovascular Disease Detection and Classification

© 2022 by IJETT Journal
Volume-70 Issue-12
Year of Publication : 2022
Author : S. Sivasubramaniam, S. P. Balamurugan
DOI : 10.14445/22315381/IJETT-V70I12P214

How to Cite?

S. Sivasubramaniam, S. P. Balamurugan, "Nature Inspired Optimization with Hybrid Machine Learning Model for Cardiovascular Disease Detection and Classification," International Journal of Engineering Trends and Technology, vol. 70, no. 12, pp. 127-137, 2022. Crossref,

Cardiovascular disease can be considered a lethal disease which affects people all over the globe. Accurate and prompt heart disease prediction can assist physicians in decision-making. Therefore, machine learning (ML) models can be applied to examine medical data for the data classification process. Since the medical dataset comprises repetitive and unwanted features affecting the classification performance, feature selection (FS) techniques can be employed. Recently, several works have applied metaheuristic algorithms for the FS process. This study presents a new mayfly optimization-based FS with a hybrid ML (MFOFS-HML) model for heart disease detection and classification. The presented MFOFS-HML model applies data pre-processing to convert the actual data into a useful format. In addition, the MFOFS technique is used for the effective selection of features from the pre-processed data. Finally, the hybrid convolutional neural network (CNN) with Hopfield neural network (HNN) hybrid (CNN-HNN) mechanism is employed for the detection and classification process. The CNN-HNN model involves the inclusion of the HNN model at the end of the CNN layer, which helps improve the classification performance with the MFO-FS technique showing the novelty of the work. The experimental validation of the MFOFS-HML model is tested under two benchmark datasets, Cleveland and Framingham datasets. A brief comparison study reported the enhanced outcomes of the presented MFOFS-HML algorithm over recent methods.

Heart disease prediction, Machine learning; Hybrid model, Feature selection, Metaheuristics.

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