ArticleSensors (Basel, Switzerland)2022
Data-Driven Machine-Learning Methods for Diabetes Risk Prediction.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning and artificial intelligence in type 2 diabetes prediction: a comprehensive 33-year bibliometric and literature analysis.Frontiers in digital health · 2025Pooled it
- A Sophisticated Onscreen Smart Framework for Predicting Diabetes in Remote Healthcare.Diagnostics (Basel, Switzerland) · 2026Article
- Data-driven diabetes mellitus prediction and management: a comparative evaluation of decision tree classifier and artificial neural network models along with statistical analysis.Scientific reports · 2025Article
- Prediction of coronary heart disease based on klotho levels using machine learning.Scientific reports · 2025Article
- Next-generation diabetes diagnosis and personalized diet-activity management: A hybrid ensemble paradigm.PloS one · 2025Article
- Enhancing diabetes risk prediction through focal active learning and machine learning models.PloS one · 2025Article
- Risk Prediction of high blood glucose among women (15-49 years) and men (15-54 years) in India: An analysis from National Family Health Survey-5 (2019-21).Journal of family medicine and primary care · 2024Article
- Comparative study of ten machine learning algorithms for short-term forecasting in gas warning systems.Scientific reports · 2024Article
- Proactive Identification of Patients with Diabetes at Risk of Uncontrolled Outcomes during a Diabetes Management Program: Conceptualization and Development Study Using Machine Learning.JMIR formative research · 2024Article
- A feature optimization study based on a diabetes risk questionnaire.Frontiers in public health · 2024Article
- Diabetes risk prediction model based on community follow-up data using machine learning.Preventive medicine reports · 2023Article
- Using Machine Learning for the Risk Factors Classification of Glycemic Control in Type 2 Diabetes Mellitus.Healthcare (Basel, Switzerland) · 2023Article
- Analysis of Publication Activity and Research Trends in the Field of AI Medical Applications: Network Approach.International journal of environmental research and public health · 2023Article
- Long-Term Coronary Artery Disease Risk Prediction with Machine Learning Models.Sensors (Basel, Switzerland) · 2023Article
- Efficient Data-Driven Machine Learning Models for Cardiovascular Diseases Risk Prediction.Sensors (Basel, Switzerland) · 2023Article
- Automatic inference of hypoglycemia causes in type 1 diabetes: a feasibility study.Frontiers in clinical diabetes and healthcare · 2023Article
- Supervised Machine Learning Models to Identify Early-Stage Symptoms of SARS-CoV-2.Sensors (Basel, Switzerland) · 2022Article
- Metabolomic Selection in the Progression of Type 2 Diabetes Mellitus: A Genetic Algorithm Approach.Diagnostics (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
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 OpenQuestion graph.