Why the study?
The study aimed to develop an automated machine learning-based network model for heart disease prediction with better accuracy.
Does a HANN model with MOSHO and WSO-LDA improve heart disease prediction accuracy and processing time in an IoMT environment?
Does a HANN model with MOSHO and WSO-LDA improve heart disease prediction accuracy and processing time in an IoMT environment?
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The proposed HANN model with WSO-LDA feature selection aims to improve the accuracy and reduce the processing time of automated heart disease detection in an IoMT environment.
The proposed work aims to develop an automated machine learning based network model for heart disease prediction with better accuracy. In the pre-processed data, the most significant features are selected using the White Shark Optimization based Linear Discriminant Analysis (WSO-LDA) technique, reducing computational complexity. Finally, the selected features are fed to the Hybrid Artificial Neural Network (HANN) with a Multi-Objective Spotted Hyena optimization (MOSHO) based classification stage. This stage classifies heart disease with minimized processing time.
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Karuppuchamy et al. (2023) studied this question.
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