Application of Machine Learning Models for Early Detection and Accurate Classification of Type 2 Diabetes

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 model...

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Bibliographic Details
Published in:Diagnostics (Basel) 2023-07, Vol.13 (14), p.2383
Main Authors: Iparraguirre-Villanueva, Orlando, Espinola-Linares, Karina, Flores Castañeda, Rosalynn Ornella, Cabanillas-Carbonell, Michael
Format: Article
Language:English
Subjects:
Accuracy
Adults
Algorithms
analysis
Artificial intelligence
Blood sugar
Classification
Datasets
Diabetes
Diabetics
Disease
Insulin
Machine learning
Mathematical functions
Medical research
Medicine, Experimental
modeling
Neural networks
Patients
Risk factors
Type 2 diabetes
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Summary: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.
ISSN:2075-4418
2075-4418
DOI:10.3390/diagnostics13142383