糖尿病
决策树
逻辑回归
朴素贝叶斯分类器
支持向量机
人工智能
随机森林
体质指数
计算机科学
2型糖尿病
机器学习
医学
内科学
内分泌学
作者
Orlando Iparraguirre-Villanueva,Karina Espinola-Linares,Rosalynn Ornella Flores-Castañeda,Michael Cabanillas-Carbonell
出处
期刊:Diagnostics
[MDPI AG]
日期:2023-07-15
卷期号:13 (14): 2383-2383
被引量:16
标识
DOI:10.3390/diagnostics13142383
摘要
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.
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