ArticleInternational journal of environmental research and public health2022
Early Prediction of Diabetes Using an Ensemble of Machine Learning Models.
Article in International journal of environmental research and public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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27 citing papers in PubMed, 144 citations in OpenAlex.
- Development of an Interpretable Triage Tool for Colorectal Polyp Risk Stratification Within a Population-Based Screening Program: Machine Learning Approach.JMIR medical informatics · 2026Article
- An Optimized and Explainable Machine Learning Framework for Diabetes Prediction Using Marine Predators Algorithm and SHAP.Journal of personalized medicine · 2026Article
- A hybrid BiLSTM and rule-based system for integrated diabetes prediction and personalized guidance.PloS one · 2026Article
- Article
- Enhanced diabetes prediction using CTGAN-MLP approach on body composition data.Scientific reports · 2025Article
- Evolution of diabetes prediction using the fusion of ANOVA, ADASYN technique and XGBoost based on body composition data.Journal of diabetes and metabolic disorders · 2025Article
- Advancing shock prediction: leveraging prior knowledge and self-controlled data for enhanced model accuracy and generalizability.BMC medical informatics and decision making · 2025Article
- Health Care Professionals and Data Scientists' Perspectives on a Machine Learning System to Anticipate and Manage the Risk of Decompensation From Patients With Heart Failure: Qualitative Interview Study.Journal of medical Internet research · 2025Article
- GBWOEM: A Gradient-Based Weight Optimization Model for Improved Predictive Accuracy in Healthcare.F1000Research · 2025Article
- Leveraging a hybrid convolutional gated recursive diabetes prediction and severity grading model through a mobile app.PeerJ. Computer science · 2025Article
- Sex-Specific Ensemble Models for Type 2 Diabetes Classification in the Mexican Population.Diabetes, metabolic syndrome and obesity : targets and therapy · 2025Article
- Enhancing diabetes risk prediction through focal active learning and machine learning models.PloS one · 2025Article
- Enhanced diabetes prediction using skip-gated recurrent unit with gradient clipping approach.Frontiers in endocrinology · 2025Article
- A Differential Evolution-Based Optimized Ensemble for Balanced and Imbalanced Medical Datasets.F1000Research · 2025Article
- Leveraging Shapley Additive Explanations for Feature Selection in Ensemble Models for Diabetes Prediction.Bioengineering (Basel, Switzerland) · 2024Article
- Robust diabetic prediction using ensemble machine learning models with synthetic minority over-sampling technique.Scientific reports · 2024Article
- Implications of Big Data Analytics, AI, Machine Learning, and Deep Learning in the Health Care System of Bangladesh: Scoping Review.Journal of medical Internet research · 2024Article
- An automated approach to predict diabetic patients using KNN imputation and effective data mining techniques.BMC medical research methodology · 2024Article
- Predicting diabetes in adults: identifying important features in unbalanced data over a 5-year cohort study using machine learning algorithm.BMC medical research methodology · 2024Article
- Ensemble Algorithm for Risk Prediction of Clinical Failure After Anterior Cruciate Ligament Reconstruction.Orthopaedic journal of sports medicine · 2024Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Diabetes is one of the most rapidly spreading diseases in the world, resulting in an array of significant complications, including cardiovascular disease, kidney failure, diabetic retinopathy, and neuropathy, among others, which contribute to an increase in morbidity and mortality rate. If diabetes is diagnosed at an early stage, its severity and underlying risk factors can be significantly reduced. However, there is a shortage of labeled data and the occurrence of outliers or data missingness in clinical datasets that are reliable and effective for diabetes prediction, making it a challenging endeavor. Therefore, we introduce a newly labeled diabetes dataset from a South Asian nation (Bangladesh). In addition, we suggest an automated classification pipeline that includes a weighted ensemble of machine learning (ML) classifiers: Naive Bayes (NB), Random Forest (RF), Decision Tree (DT), XGBoost (XGB), and LightGBM (LGB). Grid search hyperparameter optimization is employed to tune the critical hyperparameters of these ML models. Furthermore, missing value imputation, feature selection, and K-fold cross-validation are included in the framework design. A statistical analysis of variance (ANOVA) test reveals that the performance of diabetes prediction significantly improves when the proposed weighted ensemble (DT + RF + XGB + LGB) is executed with the introduced preprocessing, with the highest accuracy of 0.735 and an area under the ROC curve (AUC) of 0.832. In conjunction with the suggested ensemble model, our statistical imputation and RF-based feature selection techniques produced the best results for early diabetes prediction. Moreover, the presented new dataset will contribute to developing and implementing robust ML models for diabetes prediction utilizing population-level data.
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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 OpenQuestion graph.