Matboli Marwa, Khaled Abdelrahman, Ahmed Manar Fouad, Ahmed Manar Yehia, Khaled Radwa, Elmakromy Gena M, Ghani Amani Mohamed Abdel, El-Shafei Marwa M, Abdelhalim Marwa Ramadan M, Gwad Asmaa Mohamed Abd El
Department of Medical Biochemistry and Molecular Biology, Faculty of Medicine, Ain Shams University, Cairo, 11566, Egypt.
Bioinformatics Group, Center of Informatics Sciences (CIS), School of Information Technology and Computer Sciences, Nile University, Giza, Egypt.
Diabetol Metab Syndr. 2025 Jun 18;17(1):227. doi: 10.1186/s13098-025-01786-6.
Diabetes mellitus, a global health concern with severe complications, demands early detection and precise staging for effective management. Machine learning approaches, combined with bioinformatics, offer promising avenues for enhancing diagnostic accuracy and identifying key biomarkers.
This study employed a multi-class classification framework to classify patients across four health states: healthy, prediabetes, type 2 Diabetes Mellitus (T2DM) without complications, and T2DM with complications. Three models were developed using molecular markers, biochemical markers, and a combined model of both. Five machine learning classifiers were applied: Random Forest (RF), Extra Tree Classifier, Quadratic Discriminant Analysis, Naïve Bayes, and Light Gradient Boosting Machine. To improve the robustness and precision of the classification, Recursive Feature Elimination with Cross-Validation (RFECV) and a fivefold cross-validation were used. The multi-class classification approach enabled effective discrimination between the four diabetes stages.
The top contributing features identified for the combined model through RFECV included three molecular markers-miR342, NFKB1, and miR636-and two biochemical markers the albumin-to-creatinine ratio and HDLc, indicating their strong association with diabetes progression. The Extra Trees Classifier achieved the highest performance across all models, with an AUC value of 0.9985 (95% CI: [0.994-1.000]). This classifier outperformed other models, demonstrating its robustness and applicability for precise diabetes staging.
These findings underscore the value of integrating machine learning with molecular and biochemical markers for the accurate classification of diabetes stages, supporting a potential shift toward more personalized diabetes management.
糖尿病是一个全球性的健康问题，伴有严重并发症，需要早期检测和精确分期以进行有效管理。机器学习方法与生物信息学相结合，为提高诊断准确性和识别关键生物标志物提供了有前景的途径。
本研究采用多类分类框架，将患者分为四种健康状态：健康、糖尿病前期、无并发症的2型糖尿病（T2DM）和有并发症的T2DM。使用分子标志物、生化标志物以及两者的组合模型开发了三种模型。应用了五种机器学习分类器：随机森林（RF）、极端随机树分类器、二次判别分析、朴素贝叶斯和轻梯度提升机。为了提高分类的稳健性和精度，使用了带交叉验证的递归特征消除（RFECV）和五折交叉验证。多类分类方法能够有效区分四个糖尿病阶段。
通过RFECV为组合模型确定的主要贡献特征包括三个分子标志物——miR342、NFKB1和miR636——以及两个生化标志物——白蛋白与肌酐比值和高密度脂蛋白胆固醇，表明它们与糖尿病进展密切相关。极端随机树分类器在所有模型中表现最佳，AUC值为0.9985（95%CI：[0.994 - 1.000]）。该分类器优于其他模型，证明了其在精确糖尿病分期方面的稳健性和适用性。
这些发现强调了将机器学习与分子和生化标志物相结合用于糖尿病阶段准确分类的价值，支持向更个性化糖尿病管理的潜在转变。
