Advances in Artificial Intelligence forDiabetes Prediction: Insights from a Systematic Literature Review

• URL: http://arxiv.org/abs/2412.14736v1
• Date: Thu, 19 Dec 2024 11:09:10 GMT
• Title: Advances in Artificial Intelligence forDiabetes Prediction: Insights from a Systematic Literature Review
• Authors: Pir Bakhsh Khokhar, Carmine Gravino, Fabio Palomba,
• Abstract summary: This systematic review explores the use of machine learning (ML) in predicting diabetes, focusing on datasets, algorithms, training methods, and evaluation metrics. The review assesses the performance of ML algorithms like CNN, SVM, Logistic Regression, and XGBoost in predicting diabetes outcomes.
• Score: 8.984498754808792
• License:
• Abstract: 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.

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