ArticleBMC medical informatics and decision making2022
Diabetes mellitus risk prediction in the presence of class imbalance using flexible machine learning methods.
Article in BMC medical informatics and decision making, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it, 54 citations in OpenAlex.
- Machine learning and artificial intelligence in type 2 diabetes prediction: a comprehensive 33-year bibliometric and literature analysis.Frontiers in digital health · 2025Pooled it
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- A comparative study of quantum-inspired PSO and EA with their binary variants for heart disease classification.Scientific reports · 2026Article
- An interpretable progressive residual network for automated multiclass diabetes diagnosis.Scientific reports · 2026Article
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- Predictive voting model for early diagnosis of diabetes dataset.Frontiers in cardiovascular medicine · 2026Article
- 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
- 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
- Decoding 2.3 million ECGs: interpretable deep learning for advancing cardiovascular diagnosis and mortality risk stratification.European heart journal. Digital health · 2024Article
- The predictive power of data: machine learning analysis for Covid-19 mortality based on personal, clinical, preclinical, and laboratory variables in a case-control study.BMC infectious diseases · 2024Article
- Robust predictive framework for diabetes classification using optimized machine learning on imbalanced datasets.Frontiers in artificial intelligence · 2024Article
- Stacking with Recursive Feature Elimination-Isolation Forest for classification of diabetes mellitus.PloS one · 2024Article
- Implementing a Novel Machine Learning System for Nutrition Education in Diabetes Mellitus Nutritional Clinic: Predicting 1-Year Blood Glucose Control.Bioengineering (Basel, Switzerland) · 2023Article
- AUD-DSS: a decision support system for early detection of patients with alcohol use disorder.BMC bioinformatics · 2023Article
- Classification of imbalanced data using machine learning algorithms to predict the risk of renal graft failures in Ethiopia.BMC medical informatics and decision making · 2023Article
- Explainable Machine Learning Techniques To Predict Amiodarone-Induced Thyroid Dysfunction Risk: Multicenter, Retrospective Study With External Validation.Journal of medical Internet research · 2023Article
- Recent applications of machine learning and deep learning models in the prediction, diagnosis, and management of diabetes: a comprehensive review.Diabetology & metabolic syndrome · 2022Review
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5 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundEarly detection and prediction of type two diabetes mellitus incidence by baseline measurements could reduce associated complications in the future. The low incidence rate of diabetes in comparison with non-diabetes makes accurate prediction of minority diabetes class more challenging.
methodsDeep neural network (DNN), extremely gradient boosting (XGBoost), and random forest (RF) performance is compared in predicting minority diabetes class in Tehran Lipid and Glucose Study (TLGS) cohort data. The impact of changing threshold, cost-sensitive learning, over and under-sampling strategies as solutions to class imbalance have been compared in improving algorithms performance.
resultsDNN with the highest accuracy in predicting diabetes, 54.8%, outperformed XGBoost and RF in terms of AUROC, g-mean, and f1-measure in original imbalanced data. Changing threshold based on the maximum of f1-measure improved performance in g-mean, and f1-measure in three algorithms. Repeated edited nearest neighbors (RENN) under-sampling in DNN and cost-sensitive learning in tree-based algorithms were the best solutions to tackle the imbalance issue. RENN increased ROC and Precision-Recall AUCs, g-mean and f1-measure from 0.857, 0.603, 0.713, 0.575 to 0.862, 0.608, 0.773, 0.583, respectively in DNN. Weighing improved g-mean and f1-measure from 0.667, 0.554 to 0.776, 0.588 in XGBoost, and from 0.659, 0.543 to 0.775, 0.566 in RF, respectively. Also, ROC and Precision-Recall AUCs in RF increased from 0.840, 0.578 to 0.846, 0.591, respectively.
conclusionG-mean experienced the most increase by all imbalance solutions. Weighing and changing threshold as efficient strategies, in comparison with resampling methods are faster solutions to handle class imbalance. Among sampling strategies, under-sampling methods had better performance than others.
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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.