ArticleFrontiers in artificial intelligence2024
Robust predictive framework for diabetes classification using optimized machine learning on imbalanced datasets.
Article in Frontiers in artificial intelligence, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- Article
- An Effective Model-Based Voting Classifier for Diabetes Mellitus Classification.Bioengineering (Basel, Switzerland) · 2026Article
- Dynamic biomarker-based machine learning model predicts short-term treatment response in multiple myeloma.Journal of translational medicine · 2026Article
- Early Type 2 diabetes risk prediction using explainable machine learning in a two-stage approach.Frontiers in digital health · 2026Article
- Non-Glycemic Clinical Data for Type 2 Diabetes Detection in Mexican Adults: A Comparative Analysis of Atherogenic Indices, Statistical Transformations, and Machine Learning Algorithms.Diagnostics (Basel, Switzerland) · 2025Article
- Explainable multi stream deep learning for fine grained camel breed classification using a Novel Arabian and Non Arabian dataset.Scientific reports · 2025Article
- Ensemble neural network modelling for stratified HbA1c prediction: Integrating past glucose measurement as a predictor of glycaemic control.Bioinformation · 2025Article
- Enhanced diabetes prediction using skip-gated recurrent unit with gradient clipping approach.Frontiers in endocrinology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Diabetes prediction using clinical datasets is crucial for medical data analysis. However, class imbalances, where non-diabetic cases dominate, can significantly affect machine learning model performance, leading to biased predictions and reduced generalization. Methods: A novel predictive framework employing cutting-edge machine learning algorithms and advanced imbalance handling techniques was developed. The framework integrates feature engineering and resampling strategies to enhance predictive accuracy. Results: Rigorous testing was conducted on three datasets-PIMA, Diabetes Dataset 2019, and BIT_2019-demonstrating the robustness and adaptability of the methodology across varying data environments. Discussion: The experimental results highlight the critical role of model selection and imbalance mitigation in achieving reliable and generalizable diabetes predictions. This study offers significant contributions to medical informatics by proposing a robust data-driven framework that addresses class imbalance challenges, thereby advancing diabetes prediction accuracy.
Indexed as
Identifiers
What Socratic holds
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 Socratic graph.