Iparraguirre-Villanueva Orlando, Espinola-Linares Karina, Flores Castañeda Rosalynn Ornella, Cabanillas-Carbonell Michael
Facultad de Ingeniería y Arquitectura, Universidad Autónoma del Perú, Lima 15842, Peru.
Facultad de Ingeniería, Universidad Tecnológica del Perú, Chimbote 02710, Peru.
Diagnostics (Basel). 2023 Jul 15;13(14):2383. doi: 10.3390/diagnostics13142383.
Early detection of diabetes is essential to prevent serious complications in patients. The purpose of this work is to detect and classify type 2 diabetes in patients using machine learning (ML) models, and to select the most optimal model to predict the risk of diabetes. In this paper, five ML models, including K-nearest neighbor (K-NN), Bernoulli Naïve Bayes (BNB), decision tree (DT), logistic regression (LR), and support vector machine (SVM), are investigated to predict diabetic patients. A Kaggle-hosted Pima Indian dataset containing 768 patients with and without diabetes was used, including variables such as number of pregnancies the patient has had, blood glucose concentration, diastolic blood pressure, skinfold thickness, body insulin levels, body mass index (BMI), genetic background, diabetes in the family tree, age, and outcome (with/without diabetes). The results show that the K-NN and BNB models outperform the other models. The K-NN model obtained the best accuracy in detecting diabetes, with 79.6% accuracy, while the BNB model obtained 77.2% accuracy in detecting diabetes. Finally, it can be stated that the use of ML models for the early detection of diabetes is very promising.
早期发现糖尿病对于预防患者出现严重并发症至关重要。这项工作的目的是使用机器学习（ML）模型对患者的2型糖尿病进行检测和分类，并选择最优化的模型来预测糖尿病风险。本文研究了五种ML模型，包括K近邻（K-NN）、伯努利朴素贝叶斯（BNB）、决策树（DT）、逻辑回归（LR）和支持向量机（SVM），以预测糖尿病患者。使用了一个由Kaggle托管的皮马印第安人数据集，其中包含768名患有和未患糖尿病的患者，包括患者的怀孕次数、血糖浓度、舒张压、皮褶厚度、体内胰岛素水平、体重指数（BMI）、遗传背景、家族糖尿病史、年龄以及结果（是否患有糖尿病）等变量。结果表明，K-NN和BNB模型优于其他模型。K-NN模型在检测糖尿病方面获得了最佳准确率，为79.6%，而BNB模型在检测糖尿病方面的准确率为77.2%。最后，可以说使用ML模型进行糖尿病的早期检测非常有前景。
