Department of Computer Engineering and Informatics, University of Patras, 26504 Patras, Greece.
Sensors (Basel). 2022 Jul 15;22(14):5304. doi: 10.3390/s22145304.
Diabetes mellitus is a chronic condition characterized by a disturbance in the metabolism of carbohydrates, fats and proteins. The most characteristic disorder in all forms of diabetes is hyperglycemia, i.e., elevated blood sugar levels. The modern way of life has significantly increased the incidence of diabetes. Therefore, early diagnosis of the disease is a necessity. Machine Learning (ML) has gained great popularity among healthcare providers and physicians due to its high potential in developing efficient tools for risk prediction, prognosis, treatment and the management of various conditions. In this study, a supervised learning methodology is described that aims to create risk prediction tools with high efficiency for type 2 diabetes occurrence. A features analysis is conducted to evaluate their importance and explore their association with diabetes. These features are the most common symptoms that often develop slowly with diabetes, and they are utilized to train and test several ML models. Various ML models are evaluated in terms of the Precision, Recall, F-Measure, Accuracy and AUC metrics and compared under 10-fold cross-validation and data splitting. Both validation methods highlighted Random Forest and K-NN as the best performing models in comparison to the other models.
糖尿病是一种以碳水化合物、脂肪和蛋白质代谢紊乱为特征的慢性疾病。所有类型糖尿病中最典型的紊乱是高血糖，即血糖水平升高。现代生活方式显著增加了糖尿病的发病率。因此，早期诊断疾病是必要的。由于机器学习 (ML) 在开发用于风险预测、预后、治疗和管理各种疾病的高效工具方面具有很高的潜力，因此在医疗保健提供者和医生中得到了广泛的关注。在这项研究中，描述了一种监督学习方法，旨在创建用于 2 型糖尿病发生的高效风险预测工具。进行特征分析以评估其重要性并探索其与糖尿病的关联。这些特征是糖尿病常见的且通常发展缓慢的症状，用于训练和测试几种 ML 模型。根据 Precision、Recall、F-Measure、Accuracy 和 AUC 指标评估各种 ML 模型，并在 10 倍交叉验证和数据分割下进行比较。两种验证方法都强调随机森林和 K-NN 是表现最佳的模型，优于其他模型。
