Nature-Inspired Metaheuristic Algorithm with deep learning for Healthcare Data Analysis

Hanan T. Halawani; Aisha M. Mashraqi; Yousef Asiri; Adwan A. Alanazi; Salem Alkhalaf; Gyanendra Prasad Joshi; Hanan T. Halawani; Aisha M. Mashraqi; Yousef Asiri; Adwan A. Alanazi; Salem Alkhalaf; Gyanendra Prasad Joshi

doi:10.3934/math.2024618

AIMS Mathematics

2024, Volume 9, Issue 5: 12630-12649. doi: 10.3934/math.2024618

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Research article Special Issues

Nature-Inspired Metaheuristic Algorithm with deep learning for Healthcare Data Analysis

1.
Department of Computer Science, College of Computer Science and Information Systems, Najran University, Najran, 61441, Saudi Arabia
2.
Department of Computer Science and Information, College of Computer Science and Engineering, University of Hail, Hail, Saudi Arabia
3.
Department of Computer, College of Science and Arts in Ar Rass, Qassim University, Ar Rass, Saudi Arabia
4.
Department of Computer Science and Engineering, Sejong University, Seoul 05006, Republic of Korea

Received: 28 November 2023 Revised: 22 February 2024 Accepted: 05 March 2024 Published: 01 April 2024
MSC : 11Y40

Cardiovascular disease (CVD) detection using deep learning (DL) includes leveraging advanced neural network (NN) models to analyze medical data, namely imaging, electrocardiograms (ECGs), and patient records. This study introduces a new Nature Inspired Metaheuristic Algorithm with Deep Learning for Healthcare Data Analysis (NIMADL-HDA) technique. The NIMADL-HDA technique examines healthcare data for the recognition and classification of CVD. In the presented NIMADL-HDA technique, Z-score normalization was initially performed to normalize the input data. In addition, the NIMADL-HDA method made use of a barnacle mating optimizer (BMO) for the feature selection (FS) process. For healthcare data classification, a convolutional long short-term memory (CLSTM) model was employed. At last, the prairie dog optimization (PDO) algorithm was exploited for the optimal hyperparameter selection procedure. The experimentation outcome analysis of the NIMADL-HDA technique was verified on a benchmark healthcare dataset. The obtained outcomes stated that the NIMADL-HDA technique reached an effectual performance over other models. The NIMADL-HDA method provides an adaptable and sophisticated solution for healthcare data analysis, aiming to improve the interpretability and accuracy of the algorithm in terms of medical applications.
- Cardiovascular disease,
- deep learning,
- metaheuristic,
- prairie dog optimization,
- medical images
Citation: Hanan T. Halawani, Aisha M. Mashraqi, Yousef Asiri, Adwan A. Alanazi, Salem Alkhalaf, Gyanendra Prasad Joshi. Nature-Inspired Metaheuristic Algorithm with deep learning for Healthcare Data Analysis[J]. AIMS Mathematics, 2024, 9(5): 12630-12649. doi: 10.3934/math.2024618

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Abstract

Cardiovascular disease (CVD) detection using deep learning (DL) includes leveraging advanced neural network (NN) models to analyze medical data, namely imaging, electrocardiograms (ECGs), and patient records. This study introduces a new Nature Inspired Metaheuristic Algorithm with Deep Learning for Healthcare Data Analysis (NIMADL-HDA) technique. The NIMADL-HDA technique examines healthcare data for the recognition and classification of CVD. In the presented NIMADL-HDA technique, Z-score normalization was initially performed to normalize the input data. In addition, the NIMADL-HDA method made use of a barnacle mating optimizer (BMO) for the feature selection (FS) process. For healthcare data classification, a convolutional long short-term memory (CLSTM) model was employed. At last, the prairie dog optimization (PDO) algorithm was exploited for the optimal hyperparameter selection procedure. The experimentation outcome analysis of the NIMADL-HDA technique was verified on a benchmark healthcare dataset. The obtained outcomes stated that the NIMADL-HDA technique reached an effectual performance over other models. The NIMADL-HDA method provides an adaptable and sophisticated solution for healthcare data analysis, aiming to improve the interpretability and accuracy of the algorithm in terms of medical applications.

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