系统梳理机器学习在糖尿病预测中的应用与挑战
Advances in Artificial Intelligence forDiabetes Prediction: Insights from a Systematic Literature Review
- 综合分析多种公开数据集与主流算法表现
- 验证XGBoost、SVM等模型在糖尿病预测中达90%以上准确率
- 强调跨学科合作与伦理问题对模型落地的关键作用
本系统综述探讨了机器学习（ML）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。研究涵盖新加坡糖尿病视网膜病变筛查项目、REPLACE-BG、国家健康与营养调查及皮马印第安人糖尿病数据库等数据集。评估了卷积神经网络（CNN）、支持向量机（SVM）、逻辑回归和极端梯度提升（XGBoost）等算法在糖尿病预测中的表现。研究强调了跨学科协作与伦理考量在基于机器学习的糖尿病预测模型中的重要性。
原文摘要 · Abstract (English)
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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