Du Qinyuan, Wang Dongli, Zhang Yimin
Key Laboratory of Traditional Chinese Medicine Classical Theory, Ministry of Education, Shandong University of Traditional Chinese Medicine, Jinan, China.
Front Med (Lausanne). 2024 Aug 7;11:1425305. doi: 10.3389/fmed.2024.1425305. eCollection 2024.
The traditional complications of diabetes are well known and continue to pose a considerable burden to millions of people with diabetes mellitus (DM). With the continuous accumulation of medical data and technological advances, artificial intelligence has shown great potential and advantages in the prediction, diagnosis, and treatment of DM. When DM is diagnosed, some subjective factors and diagnostic methods of doctors will have an impact on the diagnostic results, so the use of artificial intelligence for fast and effective early prediction of DM patients can provide decision-making support to doctors and give more accurate treatment services to patients in time, which is of great clinical medical significance and practical significance. In this paper, an adaptive Stacking ensemble model is proposed based on the theory of "error-ambiguity decomposition," which can adaptively select the base classifiers from the pre-selected models. The adaptive Stacking ensemble model proposed in this paper is compared with KNN, SVM, RF, LR, DT, GBDT, XGBoost, LightGBM, CatBoost, MLP and traditional Stacking ensemble models. The results showed that the adaptive Stacking ensemble model achieved the best performance in five evaluation metrics: accuracy, precision, recall, F1 value and AUC value, which were 0.7559, 0.7286, 0.8132, 0.7686 and 0.8436. The model can effectively predict DM patients and provide a reference value for the screening and diagnosis of clinical DM.
糖尿病的传统并发症广为人知，并且继续给数百万糖尿病患者带来相当大的负担。随着医学数据的不断积累和技术进步，人工智能在糖尿病的预测、诊断和治疗方面展现出了巨大的潜力和优势。在糖尿病被诊断时，医生的一些主观因素和诊断方法会对诊断结果产生影响，因此利用人工智能对糖尿病患者进行快速有效的早期预测，能够为医生提供决策支持，并及时为患者提供更精准的治疗服务，这具有重大的临床医学意义和实际意义。本文基于“误差-模糊度分解”理论提出了一种自适应Stacking集成模型，该模型能够从预选模型中自适应地选择基分类器。将本文提出的自适应Stacking集成模型与KNN、SVM、RF、LR、DT、GBDT、XGBoost、LightGBM、CatBoost、MLP以及传统的Stacking集成模型进行比较。结果表明，自适应Stacking集成模型在准确率、精确率、召回率、F1值和AUC值这五个评估指标上取得了最佳性能，分别为0.7559、0.7286、0.8132、0.7686和0.8436。该模型能够有效地预测糖尿病患者，为临床糖尿病的筛查和诊断提供参考价值。
