Volume 7 • Issue 2 • PP: P. 01 –12 • 2026
A Review of Artificial Intelligence-Based Diabetes Prediction Using Machine Learning, Deep Learning, and Optimizer-Assisted Decision Support
Open Access & Copyright
© 2026 The Author(s). Published by ASPG. This article is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
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Diabetes prediction has become an important direction in clinical artificial intelligence because early identification of high-risk individuals can support preventive intervention, personalized monitoring, and improved long-term healthcare outcomes. This review examines recent machine learning, deep learning, and optimizer-assisted methodologies for diabetes prediction and related diagnostic decision support. It discusses how clinical variables, physiological measurements, biomedical signals, retinal images, population-specific attributes, and healthcare monitoring data can be transformed into predictive evidence through preprocessing, feature engineering, feature selection, model training, hyperparameter optimization, and risk interpretation. The review also emphasizes major methodological concerns, including class imbalance, model robustness, interpretability, subgroup variation, complication-oriented prediction, and deployment within connected healthcare environments. Overall, the reviewed literature indicates that effective diabetes prediction should be developed as an integrated clinical decision-support pipeline rather than as a single classifier comparison, with careful attention to data quality, feature relevance, model transparency, and practical healthcare usability.
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References
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