ArticleDiagnostics (Basel, Switzerland)2023
Application of Machine Learning Models for Early Detection and Accurate Classification of Type 2 Diabetes.
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Machine Learning Integration of Clinical and Molecular Biomarkers to Predict Vascular Complications in Type 2 Diabetes.Diagnostics (Basel, Switzerland) · 2026Article
- An Interpretable Fuzzy Distance-Based Ensemble Framework with SHAP Analysis for Clinically Transparent Prediction of Diabetes.Diagnostics (Basel, Switzerland) · 2026Article
- Comprehensive blood glucose level prediction from HbAScientific reports · 2026Article
- Mapping neighbourhood-level drivers of type 2 diabetes for precision public health using predictive and causal machine learning.Scientific reports · 2026Article
- Context matters in machine learning based disease prediction with insights from diverse clinical and symptom data.Scientific reports · 2025Article
- Diabetes Prediction Using Feature Selection Algorithms and Boosting-Based Machine Learning Classifiers.Diagnostics (Basel, Switzerland) · 2025Article
- Artificial Intelligence in Personalized Medicine for Diabetes Mellitus: A Narrative Review.Cureus · 2025Review
- Characterizing the role of early life factors in machine learning-based multimorbidity risk prediction.PLOS digital health · 2025Article
- A Simplified Machine Learning Model for Predicting Reduced Kidney Function in Thai Patients with Type 2 Diabetes: A Retrospective Study.Journal of clinical medicine · 2025Article
- Multi-modal analyses of proteomic measurements associated with type 2 diabetes from the Project Baseline Health Study.Communications medicine · 2025Article
- Exploring the potential and limitations of deep learning and explainable AI for longitudinal life course analysis.BMC public health · 2025Article
- Machine learning based prediction models for the prognosis of COVID-19 patients with DKA.Scientific reports · 2025Article
- Unveiling diabetes onset: Optimized XGBoost with Bayesian optimization for enhanced prediction.PloS one · 2025Article
- Machine Learning-Based Classification of Diabetes: Model Accuracy, Feature Importance, and Clinical Implications.Acta informatica medica : AIM : journal of the Society for Medical Informatics of Bosnia & Herzegovina : casopis Drustva za medicinsku informatiku BiH · 2025Article
- Machine Learning Models and Applications for Early Detection.Sensors (Basel, Switzerland) · 2024Review
- An enhanced diabetes prediction amidst COVID-19 using ensemble models.Frontiers in public health · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.