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.

翻译：本系统性综述探讨了机器学习在糖尿病预测中的应用，重点关注数据集、算法、训练方法和评估指标。研究审视了诸如新加坡国家糖尿病视网膜病变筛查项目、REPLACE-BG、国家健康与营养调查以及皮马印第安人糖尿病数据库等数据集。综述评估了CNN、SVM、逻辑回归和XGBoost等机器学习算法在预测糖尿病结局方面的性能。该研究强调了在基于机器学习的糖尿病预测模型中跨学科合作与伦理考量的重要性。