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

TL;DR

This systematic review analyzes machine learning applications in diabetes prediction, highlighting datasets, algorithms, and evaluation metrics, and emphasizing interdisciplinary and ethical considerations.

Contribution

It provides a comprehensive overview of ML datasets, algorithms, and evaluation methods used in diabetes prediction research.

Findings

ML algorithms like CNN, SVM, Logistic Regression, and XGBoost show varied performance.

Key datasets include Singapore Diabetic Retinopathy Screening, REPLACE-BG, NHANES, and Pima.

Interdisciplinary collaboration and ethics are crucial in ML-based diabetes prediction.

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.