Flock optimization algorithm-based deep learning model for diabetic disease detection improvement
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-converg...
| Main Authors: | , , , , |
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| Format: | Article |
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Science Publication
2024
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| Online Access: | http://psasir.upm.edu.my/id/eprint/106288/ |
| _version_ | 1848864736881410048 |
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| author | Balasubramaniyan, Divager Husin, Nor Azura Mustapha, Norwati Mohd Sharef, Nurfadhlina Mohd Aris, Teh Noranis |
| author_facet | Balasubramaniyan, Divager Husin, Nor Azura Mustapha, Norwati Mohd Sharef, Nurfadhlina Mohd Aris, Teh Noranis |
| author_sort | Balasubramaniyan, Divager |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-15T13:53:34Z |
| format | Article |
| id | upm-106288 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-15T13:53:34Z |
| publishDate | 2024 |
| publisher | Science Publication |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1062882024-05-14T13:05:21Z http://psasir.upm.edu.my/id/eprint/106288/ Flock optimization algorithm-based deep learning model for diabetic disease detection improvement Balasubramaniyan, Divager Husin, Nor Azura Mustapha, Norwati Mohd Sharef, Nurfadhlina Mohd Aris, Teh Noranis 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. Science Publication 2024 Article PeerReviewed 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 https://thescipub.com/abstract/10.3844/jcssp.2024.168.180 10.3844/jcssp.2024.168.180 |
| spellingShingle | Balasubramaniyan, Divager Husin, Nor Azura Mustapha, Norwati Mohd Sharef, Nurfadhlina Mohd Aris, Teh Noranis Flock optimization algorithm-based deep learning model for diabetic disease detection improvement |
| title | Flock optimization algorithm-based deep learning model for diabetic disease detection improvement |
| title_full | Flock optimization algorithm-based deep learning model for diabetic disease detection improvement |
| title_fullStr | Flock optimization algorithm-based deep learning model for diabetic disease detection improvement |
| title_full_unstemmed | Flock optimization algorithm-based deep learning model for diabetic disease detection improvement |
| title_short | Flock optimization algorithm-based deep learning model for diabetic disease detection improvement |
| title_sort | flock optimization algorithm-based deep learning model for diabetic disease detection improvement |
| url | http://psasir.upm.edu.my/id/eprint/106288/ http://psasir.upm.edu.my/id/eprint/106288/ http://psasir.upm.edu.my/id/eprint/106288/ |