Advances in Artificial Intelligence forDiabetes Prediction: Insights from a Systematic Literature Review
It addresses the problem of improving diabetes prediction for healthcare applications, but it is incremental as it synthesizes existing research without introducing new methods.
This systematic review examines the use of machine learning for diabetes prediction, analyzing datasets and algorithms such as CNN and XGBoost, and highlights the role of interdisciplinary collaboration and ethics in developing these models.
This systematic review explores the use of machine learning (ML) in predicting diabetes, focusing on datasets, algorithms, training methods, and evaluation metrics. It examines datasets like the Singapore National Diabetic Retinopathy Screening program, REPLACE-BG, National Health and Nutrition Examination Survey, and Pima Indians Diabetes Database. The review assesses the performance of ML algorithms like CNN, SVM, Logistic Regression, and XGBoost in predicting diabetes outcomes. The study emphasizes the importance of interdisciplinary collaboration and ethical considerations in ML-based diabetes prediction models.