Synergistic Machine Learning: Enhancing Diabetes Prediction with Hybrid Deep Learning and Ensemble Models

Gregorius Airlangga(1*),

(1) Universitas Katolik Indonesia Atma Jaya, Indonesia
(*) Corresponding Author

Abstract


Diabetes, a growing global health concern, necessitates improved predictive strategies for early and accurate detection. This study evaluates the efficacy of various machine learning and deep learning models in predicting the onset of diabetes, employing a comprehensive dataset that includes clinical and demographic variables. Traditional machine learning models such as Decision Trees, Random Forest, KNN, and XGBoost provided foundational insights, with ensemble methods showing superior performance. Furthermore, we explored the potential of deep learning by analyzing a Simple Dense Neural Network (DNN), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN). While these individual models yielded valuable findings, particularly in identifying true positive cases, they did not surpass the ensemble techniques in overall accuracy. The pinnacle of our research was the development of a Deep Learning Meta Learner that combined Random Forest and Gradient Boosting predictions, achieving near-perfect classification metrics, and underscoring the strength of model integration. Our findings advocate for a hybrid predictive approach that merges the nuanced feature detection of deep learning with the robust pattern recognition of ensemble models, providing an impactful direction for future diabetes prediction research. This study contributes to the advancement of medical informatics and aims to support healthcare professionals in delivering proactive and personalized patient care.


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References


H. R. Bharadwaj et al., “Examining the Provision of Renal Denervation Therapy in Low and Middle-Income Nations: Current Landscape, Challenges, Future Prospects-A Mini Perspective Review,” Curr. Probl. Cardiol., p. 102357, 2023.

X. Xiang et al., “Pancreatic cancer challenge in 52 Asian countries: age-centric insights and the role of modifiable risk factors (1990-2019),” Front. Oncol., vol. 13, p. 1271370, 2023.

Y. A. Al-Ajlouni et al., “The burden of Cardiovascular diseases in Jordan: a longitudinal analysis from the global burden of disease study, 1990--2019,” BMC Public Health, vol. 24, no. 1, p. 879, 2024.

S. Alam, M. K. Hasan, S. Neaz, N. Hussain, M. F. Hossain, and T. Rahman, “Diabetes Mellitus: insights from epidemiology, biochemistry, risk factors, diagnosis, complications and comprehensive management,” Diabetology, vol. 2, no. 2, pp. 36–50, 2021.

Y. Li et al., “Diabetic vascular diseases: molecular mechanisms and therapeutic strategies,” Signal Transduct. Target. Ther., vol. 8, no. 1, p. 152, 2023.

B. Vlacho, J. Rossell-Rusiñol, M. Granado-Casas, D. Mauricio, and J. Julve, “Overview on chronic complications of diabetes mellitus,” in Chronic Complications of Diabetes Mellitus, Elsevier, 2024, pp. 1–10.

H. Naz and S. Ahuja, “Deep learning approach for diabetes prediction using PIMA Indian dataset,” J. Diabetes & Metab. Disord., vol. 19, pp. 391–403, 2020.

R. V. Giglio et al., “Recent updates and advances in the use of glycated albumin for the diagnosis and monitoring of diabetes and renal, cerebro-and cardio-metabolic diseases,” J. Clin. Med., vol. 9, no. 11, p. 3634, 2020.

N. Fazakis, O. Kocsis, E. Dritsas, S. Alexiou, N. Fakotakis, and K. Moustakas, “Machine learning tools for long-term type 2 diabetes risk prediction,” ieee Access, vol. 9, pp. 103737–103757, 2021.

D. Bhatia, S. Paul, T. Acharjee, and S. S. Ramachairy, “Biosensors and their widespread impact on human health,” Sensors Int., vol. 5, p. 100257, 2024.

N.-N. Zhong et al., “Enhancing head and neck tumor management with artificial intelligence: Integration and perspectives,” in Seminars in Cancer Biology, 2023.

R. Dwivedi, D. Mehrotra, and S. Chandra, “Potential of Internet of Medical Things (IoMT) applications in building a smart healthcare system: A systematic review,” J. oral Biol. craniofacial Res., vol. 12, no. 2, pp. 302–318, 2022.

P. Sarajlic, “Physiological and lifestyle-related cardiovascular risk factors for vessels, ventricle, and valve,” 2024.

Q. Dong et al., “Metabolic signatures elucidate the effect of body mass index on type 2 diabetes,” Metabolites, vol. 13, no. 2, p. 227, 2023.

S. A Thirunavukarasu, “Novel advanced cardiovascular magnetic resonance imaging study in women with gestational diabetes mellitus and preeclampsia,” University of Leeds, 2023.

E. K. Oikonomou and R. Khera, “Machine learning in precision diabetes care and cardiovascular risk prediction,” Cardiovasc. Diabetol., vol. 22, no. 1, p. 259, 2023.

A. Tuppad and S. D. Patil, “Machine learning for diabetes clinical decision support: a review,” Adv. Comput. Intell., vol. 2, no. 2, p. 22, 2022.

V. Matzavela and E. Alepis, “Decision tree learning through a predictive model for student academic performance in intelligent m-learning environments,” Comput. Educ. Artif. Intell., vol. 2, p. 100035, 2021.

L.-I. Coman, M. Ianculescu, E.-A. Paraschiv, A. Alexandru, and I.-A. Buaduaruau, “Smart Solutions for Diet-Related Disease Management: Connected Care, Remote Health Monitoring Systems, and Integrated Insights for Advanced Evaluation,” Appl. Sci., vol. 14, no. 6, p. 2351, 2024.

L. Chen, B. Han, X. Wang, J. Zhao, W. Yang, and Z. Yang, “Machine learning methods in weather and climate applications: A survey,” Appl. Sci., vol. 13, no. 21, p. 12019, 2023.

M. Arabahmadi, R. Farahbakhsh, and J. Rezazadeh, “Deep learning for smart Healthcare—A survey on brain tumor detection from medical imaging,” Sensors, vol. 22, no. 5, p. 1960, 2022.

S. A. Alex, J. J. V. Nayahi, H. Shine, and V. Gopirekha, “Deep convolutional neural network for diabetes mellitus prediction,” Neural Comput. Appl., vol. 34, no. 2, pp. 1319–1327, 2022.

A. P. Zhao et al., “AI for Science: Predicting Infectious Diseases,” J. Saf. Sci. Resil., 2024.

G. Battineni, G. G. Sagaro, N. Chinatalapudi, and F. Amenta, “Applications of machine learning predictive models in the chronic disease diagnosis,” J. Pers. Med., vol. 10, no. 2, p. 21, 2020.

E. Aboelnaga, “Diabetes Dataset.” Kaggle, 2023.




DOI: https://doi.org/10.30645/kesatria.v5i3.457

DOI (PDF): https://doi.org/10.30645/kesatria.v5i3.457.g452

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