A Hybrid Firefly-Stacking Ensemble Model for Early Prediction of Type II Diabetes

Michael Favour EDAFEAJIROKE; Veronica Ijebusomma Osubor

doi:10.51173/ijds.v3i2.71

Authors

Michael Favour EDAFEAJIROKE 1Department of Computer Science, Faculty of Computing, University of Port Harcourt, Rivers State, Nigeria https://orcid.org/0000-0002-9418-8475
Prof. V. I. Osubor University of Benin, Benin City, Edo State, Nigeria. https://orcid.org/0000-0002-5014-3769

DOI:

https://doi.org/10.51173/ijds.v3i2.71

Keywords:

Type II Diabetes Prediction, Firefly Algorithm, Stack-ensemble Learning, Machine Learning, Feature Selection

Abstract

The increasing global prevalence of Type II Diabetes (T2D) demands an advanced predictive model, as conventional machine learning algorithms perform poorly on such complex biomedical datasets. The proposed study aims to develop a trustworthy early prediction model by integrating the Firefly Algorithm (FA) for feature selection with a comparative stacking ensemble technique. The proposed methodology uses the large-scale Behavioral Risk Factor Surveillance System (BRFSS) dataset. The FA algorithm identified the optimal combination of 16 important features to train a stack ensemble of base models using a Meta Random Forest classifier. The most important results reveal that the FA-improved Meta Random Forest classifier achieved the best possible balance between accuracy and efficiency, with 88.68% accuracy, 83.26% sensitivity, and an AUC of 94.37%. It is concluded that the hybrid strategy is an efficient and effective approach to early risk stratification in T2D and addresses an important gap in predictive medicine by combining feature selection optimization and ensemble methods. The hybrid strategy provides a platform for future validation studies across different clinical datasets, enabling proactive, data-driven interventions.

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Author Biography

Prof. V. I. Osubor, University of Benin, Benin City, Edo State, Nigeria.

Computer Science (Professor)

References

G. Prabhakar, V. R. Chintala, T. H. Reddy, and T. Ruchitha, "User-Cloud-Based vEnsemble Framework for Type-2 Diabetes Prediction with Diet Plan Suggestion," e-Prime, vol. 7, p. 100423, 2024, doi: 10.1016/j.prime.2024.100423.

O. Y. Hang, V. Wiwied, and R. Rosaida, "Diabetes Prediction using Machine Learning Ensemble Model," Journal of Advanced Research in Applied Sciences and Engineering Technology, vol. 37, no. 1, pp. 82-98, 2024, doi: 10.3390/electronics806063.

I. D. Mienye and Y. Sun, "Performance analysis of cost-sensitive learning methods with application to imbalanced medical data," Informatics in Medicine Unlocked, vol. 25, p. 100690, 2021, doi: 10.1016/j.imu.2021.100690.

Z. Zhang et al., "A novel evolutionary ensemble prediction model using harmony search and stacking for diabetes diagnosis," Journal of King Saud University Computer and Information Sciences, vol. 36, no. 1, p. 101873, 2024, doi: 10.1016/j.jksuci.2023.101873.

I. D. Mienye and Y. Sun, "A survey of ensemble learning: Concepts, algorithms, applications, and prospects," IEEE Access, vol. 10, pp. 99129–99149, 2022, doi: 10.1109/access.2022.3207287.

A. V. Daza, C. M. F. Sánchez, O. Apaza, J. D. Pinto, and K. Z. Ramos, "Stacking ensemble approach to diagnosing the disease of diabetes," Informatics in Medicine Unlocked, vol. 44, p. 101427, 2024, doi: 10.1016/j.imu.2023.101427.

V. C. S. S and A. H. S, "Nature inspired meta heuristic algorithms for optimization problems," Computing, vol. 104, no. 2, pp. 251–269, May 2021, doi: 10.1007/s00607-021-00955-5.

W. Li, Q. Zhang, and H. Zhou, "Optimized Grid Search with Early Stopping for Efficient Hyperparameter Tuning," Neural Networks, vol. 158, pp. 210-225, 2023, doi: 10.1016/j.neunet.2023.02.018.

V. Chang, M. Ganatra, K. Hall, L. Golightly, and Q. 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, doi: 10.1016/j.health.2022.100118.

M. Z. Naser and A. Z. Naser, "The firefighter algorithm for optimization problems," Neural Computing and Applications, vol. 37, no. 16, pp. 9345–9400, Feb. 2025, doi: 10.1007/s00521-025-11074-z.

J. Abdollahi and B. Nouri-Moghaddam, "Hybrid stacked ensemble combined with genetic algorithms for diabetes prediction," Iran Journal of Computer Science, vol. 5, no. 3, pp. 205–220, Mar. 2022, doi: 10.1007/s42044-022-00100-1.

M. S. Reza, R. Amin, R. Yasmin, W. Kulsum, and S. Ruhi, "Improving diabetes disease patients classification using stacking ensemble method with PIMA and local healthcare data," Heliyon, vol. 10, no. 2, p. e24536, Jan. 2024, doi: 10.1016/j.heliyon.2024.e24536.

A. Daza, C. F. P. Sánchez, G. Apaza-Perez, J. Pinto, and K. Z. Ramos, "Stacking ensemble approach to diagnosing the disease of diabetes," Informatics in Medicine Unlocked, vol. 44, p. 101427, Dec. 2023, doi: 10.1016/j.imu.2023.101427.

W. Li, Y. Peng, and K. Peng, "Diabetes prediction model based on GA-XGBoost and stacking ensemble algorithm," PLoS ONE, vol. 19, no. 9, p. e0311222, Sep. 2024, doi: 10.1371/journal.pone.0311222.

A. Dutta et al., "Early prediction of diabetes using an ensemble of machine learning models," International Journal of Environmental Research and Public Health, vol. 19, no. 19, p. 12378, Sep. 2022, doi: 10.3390/ijerph191912378.

Z. Aamir and I. Murtza, "Pre-Diabetic Diagnosis from Habitual and Medical Features using Ensemble Classification," Journal of Computing and Biomedical Informatics, vol. 5, no. 1, 2023, doi: 10.56979/501/2023.

V. Kansal, K. Upreti, J. Singh, M. Shanbhog and R. C. Poonia, "A Diabetes Detection Framework Based on Datadriven Predictive Technologies," 2025 IEEE 5th International Conference on ICT in Business Industry & Government (ICTBIG), Indore, Madhya Pradesh, India, India, 2025, pp. 1-6, doi: 10.1109/ICTBIG68706.2025.11323965.

A. Aman and R. S. Chhillar, "Optimized stacking ensemble for early-stage diabetes mellitus prediction," International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering, vol. 13, no. 6, p. 7048, Sep. 2023, doi: 10.11591/ijece.v13i6.pp7048-7055.

D. V. K and T. K. Ramesh, "Optimized stacking ensemble models for the prediction of diabetic progression," Multimedia Tools and Applications, vol. 82, no. 27, pp. 42901–42925, Apr. 2023, doi: 10.1007/s11042-023-14858-4.

K. Abnoosian, R. Farnoosh, and M. H. Behzadi, "Prediction of diabetes disease using an ensemble of machine learning multi-classifier models," BMC Bioinformatics, vol. 24, no. 1, p. 337, Sep. 2023, doi: 10.1186/s12859-023-05465-z.

M. F. Edafeajiroke, S. O. Abdulsalam, M. U. Shuaib, and R. S. Babatunde, "Development of an Intrusion Detection System using ANOVA Feature Selection and Support Vector Machine Algorithms," Computology, vol. 4, no. 1, p. 8908, 2024, doi: 10.17492/computology.v4i1.2406.

M. F. Edafeajiroke and V. I. Osubor, "Optimizing random forest for type II diabetes prediction using firefly algorithm," International Journal of Engineering Research & Technology (IJERT), vol. 15, no. 1, 2026, doi: 10.17577/IJERTV15IS010010.