A Comparative Analysis of Diabetes Prediction through Deep Learning Architectures
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Gregorius Airlangga
*
Atma Jaya Catholic University of Indonesia, Jakarta,
Indonesia
(*) Corresponding Author
Abstract
Diabetes prediction plays a vital role in healthcare, enabling early diagnosis and timely interventions to mitigate the risks associated with the disease. This study investigates the application of advanced machine learning architectures to predict diabetes using the Pima Indians Diabetes Dataset, a widely used benchmark for medical diagnostics. Five models: Deep Neural Network (DNN), Convolutional Neural Network (CNN) with Attention, LSTM with Residual Connections, Bidirectional LSTM (BiLSTM) with Attention, and GRU with Dense Layers were developed and evaluated on multiple performance metrics, including accuracy, precision, recall, F1 score, and ROC AUC. A stratified five-fold cross-validation strategy was employed to ensure robustness, while SHAP analysis was conducted to enhance interpretability. Among the models, the GRU with Dense Layers achieved superior performance, recording the highest accuracy (76.17%), F1 score (69.85%), and ROC AUC (83.52%). SHAP analysis revealed Glucose as the most influential feature, with significant interactions identified between Glucose and Pregnancies, aligning with established medical insights. Statistical analysis confirmed the reliability of the results, with all metrics demonstrating statistically significant improvements over a baseline of random chance (p < 0.05). These findings underscore the efficacy of GRU-based models in capturing complex patterns in medical data while maintaining computational efficiency. Future work will explore hybrid architectures and larger datasets to enhance generalizability and real-world applicability, contributing to more effective decision-making in healthcare.
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References
S. Y. Prasetyo and Z. N. Izdihar, “Multi-layer perceptron approach for diabetes risk prediction using BRFSS data,” in 2024 IEEE 10th International Conference on Smart Instrumentation, Measurement and Applications (ICSIMA), 2024, pp. 303–308.
W. H. Organization, Global report on hypertension: the race against a silent killer. World Health Organization, 2023.
S. Y. Prasetyo, Z. N. Izdihar, and G. Z. Nabiilah, “Analyzing Machine Learning Approaches for Diabetes Risk Prediction: Comparative Performance Assessment Using BRFSS Data,” in 2024 7th International Conference on Informatics and Computational Sciences (ICICoS), 2024, pp. 324–329.
M. G. Shlipak et al., “The case for early identification and intervention of chronic kidney disease: conclusions from a Kidney Disease: Improving Global Outcomes (KDIGO) Controversies Conference,” Kidney Int., vol. 99, no. 1, pp. 34–47, 2021.
M. MacLeod et al., “Chronic obstructive pulmonary disease exacerbation fundamentals: diagnosis, treatment, prevention and disease impact,” Respirology, vol. 26, no. 6, pp. 532–551, 2021.
C. Elendu et al., “Understanding sickle cell disease: causes, symptoms, and treatment options,” Medicine (Baltimore)., vol. 102, no. 38, p. e35237, 2023.
M. Javaid, A. Haleem, R. P. Singh, R. Suman, and S. Rab, “Significance of machine learning in healthcare: Features, pillars and applications,” Int. J. Intell. Networks, vol. 3, pp. 58–73, 2022.
R. Thirunavukarasu, R. Gnanasambandan, M. Gopikrishnan, V. Palanisamy, and others, “Towards computational solutions for precision medicine based big data healthcare system using deep learning models: A review,” Comput. Biol. Med., p. 106020, 2022.
A. F. Ashour, M. M. Fouda, Z. M. Fadlullah, and M. I. Ibrahem, “Optimized Neural Networks for Diabetes Classification Using Pima Indians Diabetes Database,” in 2024 IEEE 3rd International Conference on Computing and Machine Intelligence (ICMI), 2024, pp. 1–7.
C. C. Olisah, L. Smith, and M. Smith, “Diabetes mellitus prediction and diagnosis from a data preprocessing and machine learning perspective,” Comput. Methods Programs Biomed., vol. 220, p. 106773, 2022.
U. C. I. M. L. Repository, “Pima Indians Diabetes Database.” 2020.
A. Heidari, N. J. Navimipour, and M. Unal, “Applications of ML/DL in the management of smart cities and societies based on new trends in information technologies: A systematic literature review,” Sustain. Cities Soc., vol. 85, p. 104089, 2022.
Y. Xu, R. Quan, W. Xu, Y. Huang, X. Chen, and F. Liu, “Advances in Medical Image Segmentation: A Comprehensive Review of Traditional, Deep Learning and Hybrid Approaches,” Bioengineering, vol. 11, no. 10, p. 1034, 2024.
S. Ahmad, I. Shakeel, S. Mehfuz, and J. Ahmad, “Deep learning models for cloud, edge, fog, and IoT computing paradigms: Survey, recent advances, and future directions,” Comput. Sci. Rev., vol. 49, p. 100568, 2023.
C. Janiesch, P. Zschech, and K. Heinrich, “Machine learning and deep learning,” Electron. Mark., vol. 31, no. 3, pp. 685–695, 2021.
P. B. Weerakody, K. W. Wong, G. Wang, and W. Ela, “A review of irregular time series data handling with gated recurrent neural networks,” Neurocomputing, vol. 441, pp. 161–178, 2021.
A. de Santana Correia and E. L. Colombini, “Attention, please! A survey of neural attention models in deep learning,” Artif. Intell. Rev., vol. 55, no. 8, pp. 6037–6124, 2022.
I. Zafar et al., “Reviewing methods of deep learning for intelligent healthcare systems in genomics and biomedicine,” Biomed. Signal Process. Control, vol. 86, p. 105263, 2023.
H. Guan et al., “The role of machine learning in advancing diabetic foot: a review,” Front. Endocrinol. (Lausanne)., vol. 15, p. 1325434, 2024.
I. Smokovski, Managing diabetes in low income countries. Springer, 2021.
G. M. Dogheim and A. Hussain, “Patient care through AI-driven remote monitoring: Analyzing the role of predictive models and intelligent alerts in preventive medicine,” J. Contemp. Healthc. Anal., vol. 7, no. 1, pp. 94–110, 2023.
J. Egger et al., “Medical deep learning—A systematic meta-review,” Comput. Methods Programs Biomed., vol. 221, p. 106874, 2022.
C. El Morr, M. Jammal, H. Ali-Hassan, and W. El-Hallak, “Data preprocessing,” in Machine Learning for Practical Decision Making: A Multidisciplinary Perspective with Applications from Healthcare, Engineering and Business Analytics, Springer, 2022, pp. 117–163.
S. Bouteldja, “Hybrid System for Diabetes Prediction,” 2023.
R. Valentini, “Ontology-based Data Management in Healthcare,” 2024.
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