Yan Dapeng, Li Xiaohan, Wang Yifan, Cai Zhikuang
College of Integrated Circuit Science and Engineering, Nanjing University of Posts and Telecommunications, Nanjing, China.
Laboratory Medicine Center, The Second Affiliated Hospital, Nanjing Medical University, Nanjing, China.
Front Endocrinol (Lausanne). 2025 Jun 20;16:1593068. doi: 10.3389/fendo.2025.1593068. eCollection 2025.
The increasing global prevalence of diabetes has led to a surge in complications, significantly burdening healthcare systems and affecting patient quality of life. Early prediction of these complications is critical for timely intervention, yet existing models often rely heavily on clinical indicators while underutilizing fundamental laboratory test parameters. This study aims to bridge this gap by leveraging the 12 most frequently tested laboratory indicators in diabetic patients to develop an optimized predictive model for diabetes complications.
A comprehensive dataset was established through meticulous data collection from a high-volume tertiary hospital, followed by extensive data cleaning and classification. Various machine learning classifiers, including Random Forest, XGBoost, Support Vector Machine (SVM), and Multilayer Perceptron (MLP), were trained on this dataset to evaluate their predictive performance. We further introduced an ensemble learning model with Bayesian optimization to enhance accuracy and cost-efficiency. Additionally, feature importance analysis was conducted to refine the model by reducing testing costs while maintaining high predictive accuracy.
Our ensemble model with Bayesian optimization demonstrated superior performance, achieving over 90% accuracy in predicting various diabetic complications, with an outstanding 98.50% accuracy and 99.76% AUC for diabetic nephropathy. Feature correlation analysis enabled a refined model that not only improved predictive accuracy but also reduced overall medical costs by 2.5% through strategic feature elimination.
This study makes three key contributions: (1) Development of a high-quality dataset based on fundamental laboratory indicators, (2) Creation of a highly accurate predictive model using ensemble learning and Bayesian optimization, particularly excelling in diabetic nephropathy prediction, and (3) Implementation of a cost-efficient diagnostic approach that reduces testing expenses without compromising accuracy. The proposed model provides a strong foundation for future research and practical clinical applications, demonstrating the potential of integrating machine learning with cost-conscious medical testing.
全球糖尿病患病率不断上升，导致并发症激增，给医疗系统带来巨大负担，并影响患者生活质量。早期预测这些并发症对于及时干预至关重要，但现有模型往往严重依赖临床指标，而对基本实验室检测参数利用不足。本研究旨在通过利用糖尿病患者最常检测的12项实验室指标来填补这一空白，以开发一种优化的糖尿病并发症预测模型。
通过从一家大型三级医院精心收集数据，建立了一个综合数据集，随后进行了广泛的数据清理和分类。在该数据集上训练了各种机器学习分类器，包括随机森林、XGBoost、支持向量机（SVM）和多层感知器（MLP），以评估它们的预测性能。我们进一步引入了一种带有贝叶斯优化的集成学习模型，以提高准确性和成本效益。此外，还进行了特征重要性分析，通过降低检测成本同时保持高预测准确性来优化模型。
我们带有贝叶斯优化的集成模型表现出卓越性能，在预测各种糖尿病并发症方面准确率超过90%，对于糖尿病肾病，准确率高达98.50%，曲线下面积（AUC）为99.76%。特征相关性分析实现了一个优化模型，该模型不仅提高了预测准确性，还通过战略性特征消除将总体医疗成本降低了2.5%。
本研究做出了三项关键贡献：（1）基于基本实验室指标开发了一个高质量数据集；（2）使用集成学习和贝叶斯优化创建了一个高度准确的预测模型，尤其在糖尿病肾病预测方面表现出色；（3）实施了一种具有成本效益的诊断方法，在不影响准确性的情况下降低了检测费用。所提出的模型为未来研究和实际临床应用提供了坚实基础，展示了将机器学习与注重成本的医学检测相结合的潜力。
