@article{Halawani2024, 
author = {Hanan T. Halawani and Aisha M. Mashraqi and Yousef Asiri and Adwan A. Alanazi and Salem Alkhalaf and Gyanendra Prasad Joshi},
title = {Nature-Inspired Metaheuristic Algorithm with deep learning for Healthcare Data Analysis},
year = {2024},
journal = {AIMS Mathematics},
volume = {9},
number = {5},
pages = {12630-12649},
keywords = {Cardiovascular disease, deep learning, metaheuristic, prairie dog optimization, medical images},
url = {https://www.sciopen.com/article/10.3934/math.2024618},
doi = {10.3934/math.2024618},
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.}
}