Convolutional Neural Network based Di-Strategy Cheetah Optimization Algorithm for Automatic Diabetes Prediction
Keywords:
adaptive search agent, di-strategy cheetah optimization algorithm, convolutional neural network, min-max normalization, tent chaotic mappingAbstract
Diabetes is a chronic metabolic disease characterized by elevated blood sugar levels. Diabetes prediction leverages patient data to assess the risk of developing the condition, facilitating early diagnosis and intervention. However, existing models struggle to capture the complex interactions between risk factors due to limited feature representation, leading to inaccurate predictions. This research proposes a Convolutional Neural Network-based Di-Strategy Cheetah Optimization Algorithm (CNN-DS-COA) for automatic diabetes prediction using patient data. The COA is enhanced with tent chaotic mapping and an adaptive search agent, which improves population diversity distribution and convergence speed. Initially, the Pima Indians Diabetes Database (PIMA) and Germany datasets are employed to evaluate the performance of CNN-DS-COA. Min-max normalization is applied to scale the data within a uniform range while preserving relationships among values. The CNN is then used for automatic diabetes prediction, with DS-COA fine-tuning the CNN's parameter values effectively using two strategies. The proposed CNN-DS-COA achieves superior accuracy, with 99.90% and 99.72% on the PIMA and Frankfurt Hospital, Germany datasets, respectively, outperforming existing methods such as stacked ensemble approaches and statistical predictive models.
References
A. Doğru, S. Buyrukoğlu, M. Arı, “A hybrid super ensemble learning model for the early-stage prediction of diabetes risk,” Medical & Biological Engineering & Computing, vol. 61, issue 3, pp. 785-797, 2023. https://doi.org/10.1007/s11517-022-02749-z.
F. A. Khaleel, A. M. Al-Bakry, “Diagnosis of diabetes using machine learning algorithms,” Materials Today: Proceedings, vol. 80, no. 3, pp. 3200-3203, 2023. https://doi.org/10.1016/j.matpr.2021.07.196.
S. M Ganie, P. K. D. Pramanik, M. B. Malik, S. Mallik, H. Qin, “An ensemble learning approach for diabetes prediction using boosting techniques,” Frontiers in Genetics, vol. 14, p. 1252159, 2023. https://doi.org/10.3389/fgene.2023.1252159.
S. K. Chinnababu, A. Jayachandra, “Diabetes prediction and classification using self-adaptive evolutionary algorithm with convolutional neural network,” International Journal of Intelligent Engineering & Systems, vol. 17, issue 3, pp. 460-472, 2024. https://doi.org/10.22266/ijies2024.0630.36.
A. Naseem, R. Habib, T. Naz, M. Atif, M. Arif, and S. Allaoua Chelloug, “Novel Internet of Things based approach toward diabetes prediction using deep learning models,” Frontiers in Public Health, vol. 10, p. 914106, 2022. https://doi.org/10.3389/fpubh.2022.914106.
M. Ragab, A. S. A. M. Al-Ghamdi, B. Fakieh, H. Choudhry, R. F. Mansour, and D. Koundal, “Prediction of diabetes through retinal images using deep neural network,” Computational Intelligence and Neuroscience, vol. 2022, issue 1, p.7887908, 2022. https://doi.org/10.1155/2022/7887908.
J. J. Sonia, P. Jayachandran, A. Q. Md, S. Mohan, A. K. Sivaraman, K. F. Tee, “Machine-learning-based diabetes mellitus risk prediction using multi-layer neural network no-prop algorithm,” Diagnostics, vol. 13, issue 4, p. 723, 2023. https://doi.org/10.3390/diagnostics13040723.
V. Chang, M. A. Ganatra, K. Hall, L. Golightly, Q. A. Xu, “An assessment of machine learning models and algorithms for early prediction and diagnosis of diabetes using health indicators,” Healthcare Analytics, vol. 2, p. 100118, 2022. https://doi.org/10.1016/j.health.2022.100118.
C. C. Olisah, L. Smith, M. Smith, “Diabetes mellitus prediction and diagnosis from a data preprocessing and machine learning perspective,” Computer Methods and Programs in Biomedicine, vol. 220, p. 106773, 2022. https://doi.org/10.1016/j.cmpb.2022.106773.
A. Armghan, J. Logeshwaran, S. M. Sutharshan, K. Aliqab, M. Alsharari, S. K. Patel, “Design of biosensor for synchronized identification of diabetes using deep learning,” Results in Engineering, vol. 20, p. 101382, 2023. https://doi.org/10.1016/j.rineng.2023.101382.
Y. Xiong, L. Lin, Y. Chen, S. Salerno, Y. Li, X. Zeng, and H. Li, “Prediction of gestational diabetes mellitus in the first 19 weeks of pregnancy using machine learning techniques,” The Journal of Maternal-Fetal & Neonatal Medicine, vol. 35, issue 13, pp. 2457-2463, 2022. https://doi.org/10.1080/14767058.2020.1786517.
M. Atif, F. Anwer, and F. Talib, “An ensemble learning approach for effective prediction of diabetes mellitus using hard voting classifier,” Indian Journal of Science and Technology, vol. 15, issue 39, pp. 1978-1986, 2022. https://doi.org/10.17485/IJST/v15i39.1520.
I. Tasin, T. U. Nabil, S. Islam, and R. Khan, “Diabetes prediction using machine learning and explainable AI techniques,” Healthcare Technology Letters, vol. 10, issue 1-2, pp. 1-10, 2023. https://doi.org/10.1049/htl2.12039.
S. A. Alex, N. Z. Jhanjhi, M. Humayun, A. O. Ibrahim, and A. W. Abulfaraj, “Deep LSTM model for diabetes prediction with class balancing by SMOTE,” Electronics, vol. 11, issue 17, p. 2737, 2022. https://doi.org/10.3390/electronics11172737.
H. Zhou, Y. Xin, and S. Li, “A diabetes prediction model based on Boruta feature selection and ensemble learning,” BMC Bioinformatics, vol. 24, issue 1, p. 224, 2023. https://doi.org/10.1186/s12859-023-05300-5.
H. Naz, and S. Ahuja, “SMOTE-SMO-based expert system for type II diabetes detection using PIMA dataset,” International Journal of Diabetes in Developing Countries, vol. 42, issue 2, pp. 245-253, 2022. https://doi.org/10.1007/s13410-021-00969-x.
R. Sivashankari, M. Sudha, M. K. Hasan, R. A. Saeed, S. A. Alsuhibany, S. Abdel-Khalek, “An empirical model to predict the diabetic positive using stacked ensemble approach,” Frontiers in Public Health, vol. 9, p. 792124, 2022. https://doi.org/10.3389/fpubh.2021.792124.
U. Ahmed, G. F. Issa, M. A. Khan, S. Aftab, M.F. Khan, R. A. Said, T. M. Ghazal, and M. Ahmad, “Prediction of diabetes empowered with fused machine learning,” IEEE Access, vol. 10, pp. 8529-8538, 2022. https://doi.org/10.1109/ACCESS.2022.3142097.
M.K. Gourisaria, G. Jee, G. M. Harshvardhan, V. Singh, P. K. Singh, T. C. Workneh, “Data science appositeness in diabetes mellitus diagnosis for healthcare systems of developing nations,” IET Communications, Special Issue: Deep learning‐based intelligent communication systems: Using big data analytics, vol. 16, issue 5, pp. 532-547, 2022. https://doi.org/10.1049/cmu2.12338.
K. Azbeg, M. Boudhane, O. Ouchetto, S. J. Andaloussi, “Diabetes emergency cases identification based on a statistical predictive model,” Journal of Big Data, vol. 9, issue 1, p. 31, 2022. https://doi.org/10.1186/s40537-022-00582-7.
PIMA dataset. [Online]. Available at: https://www.kaggle.com/datasets/uciml/pima-indians-diabetes-database.
Frankfurt Hospital, Germany dataset. [Online]. Available at: https://www.kaggle.com/code/linggarmaretva/frankfurt-hospital-diabetes-with-lgbmclassifier.
A. Saha, A. R. Mishra, P. Rani, I. M. Hezam, and F. Cavallaro, “A q-rung orthopair fuzzy FUCOM double normalization-based multi-aggregation method for healthcare waste treatment method selection,” Sustainability, vol. 14, issue 7, p. 4171, 2022. https://doi.org/10.1186/s40537-022-00582-7.
J. P. Bharadiya, “Convolutional neural networks for image classification,” International Journal of Innovative Science and Research Technology, vol. 8, issue 5, pp. 673-677, 2023.
M. A. Akbari, M. Zare, R. Azizipanah-Abarghooee, S. Mirjalili, and M. Deriche, “The cheetah optimizer: A nature-inspired metaheuristic algorithm for large-scale optimization problems,” Scientific Reports, vol. 12, p. 10953, 2022. https://doi.org/10.1038/s41598-022-14338-z.
Downloads
Published
How to Cite
Issue
Section
License
International Journal of Computing is an open access journal. Authors who publish with this journal agree to the following terms:• Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
• Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
• Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
