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Flock optimization algorithm-based deep learning model for diabetic disease detection improvement


Citation

Balasubramaniyan, Divager and Husin, Nor Azura and Mustapha, Norwati and Mohd Sharef, Nurfadhlina and Mohd Aris, Teh Noranis (2024) Flock optimization algorithm-based deep learning model for diabetic disease detection improvement. Journal of Computer Science, 20 (2). pp. 168-180. ISSN 1549-3636; ESSN: 1552-6607

Abstract

Worldwide, 422 million people suffer from diabetic disease, and 1.5 million die yearly. Diabetes is a threat to people who still fail to cure or maintain it, so it is challenging to predict this disease accurately. The existing systems face data over-fitting issues, convergence problems, non-converging optimization complex predictions, and latent and predominant feature extraction. These issues affect the system's performance and reduce diabetic disease detection accuracy. Hence, the research objective is to create an improved diabetic disease detection system using a Flock Optimization Algorithm-Based Deep Learning Model (FOADLM) feature modeling approach that leverages the PIMA Indian dataset to predict and classify diabetic disease cases. The collected data is processed by a Gaussian filtering approach that eliminates irrelevant information, reducing the overfitting issues. Then flock optimization algorithm is applied to detect the sequence; this process is used to reduce the convergence and optimization problems. Finally, the recurrent neural approach is applied to classify the normal and abnormal features. The entire research implementation result is carried out with the help of the MATLAB program and the results are analyzed with accuracy, precision, recall, computational time, reliability scalability, and error rate measures like root mean square error, mean square error, and correlation coefficients. In conclusion, the system evaluation result produced 99.23 accuracy in predicting diabetic disease with the metrics.


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Additional Metadata

Item Type: Article
Divisions: Faculty of Computer Science and Information Technology
DOI Number: https://doi.org/10.3844/jcssp.2024.168.180
Publisher: Science Publication
Keywords: Diabetic diseaseclassification system; Flock optimization algorithm; Deep learning model (FOADLM); Gaussian filtering; Convergence problem; Diabetic disease detection; Swarm intelligence; Neural networks; Prediction models
Depositing User: Mr. Mohamad Syahrul Nizam Md Ishak
Date Deposited: 14 May 2024 13:05
Last Modified: 14 May 2024 13:05
Altmetrics: http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.3844%2Fjcssp.2024.168.180
URI: http://psasir.upm.edu.my/id/eprint/106288
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