用机器学习+可解释AI，92.5%准确率预测糖尿病，还能看清关键影响因素。
Towards Transparent and Accurate Diabetes Prediction Using Machine Learning and Explainable Artificial Intelligence
- 基于SMOTE和特征缩放处理数据不平衡与临床变量差异
- 集成模型测试准确率达92.5%，ROC-AUC达0.975
- 识别出年龄、BMI等五大关键预测因子，结果可解释
糖尿病是全球重要健康问题，需尽早诊断与管理。本研究提出一种结合机器学习（ML）与可解释人工智能（XAI）的糖尿病预测框架，评估模型预测准确性与可解释性。数据预处理采用合成少数类过采样技术（SMOTE）和特征缩放，针对糖尿病二分类健康指标数据集处理类别不平衡与临床特征变异问题。集成模型表现优异，测试准确率为92.50%，ROC-AUC达0.975。模型解释结果显示，体重指数（BMI）、年龄、整体健康状况、收入水平和身体活动是最重要的预测因子。研究表明，将机器学习与XAI结合，可为医疗系统构建兼具高精度与计算透明性的辅助决策工具。
原文摘要 · Abstract (English)
Diabetes mellitus (DM) is a global health issue of significance that must be diagnosed as early as possible and managed well. This study presents a framework for diabetes prediction using Machine Learning (ML) models, complemented with eXplainable Artificial Intelligence (XAI) tools, to investigate both the predictive accuracy and interpretability of the predictions from ML models. Data Preprocessing is based on the Synthetic Minority Oversampling Technique (SMOTE) and feature scaling used on the Diabetes Binary Health Indicators dataset to deal with class imbalance and variability of clinical features. The ensemble model provided high accuracy, with a test accuracy of 92.50% and an ROC-AUC of 0.975. BMI, Age, General Health, Income, and Physical Activity were the most influential predictors obtained from the model explanations. The results of this study suggest that ML combined with XAI is a promising means of developing accurate and computationally transparent tools for use in healthcare systems.
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