ArticleSensors (Basel, Switzerland)2022
An Ensemble Approach to Predict Early-Stage Diabetes Risk Using Machine Learning: An Empirical Study.
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 17 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
17 citing papers in PubMed, 133 citations in OpenAlex.
- An Optimized and Explainable Machine Learning Framework for Diabetes Prediction Using Marine Predators Algorithm and SHAP.Journal of personalized medicine · 2026Article
- Review
- A Sophisticated Onscreen Smart Framework for Predicting Diabetes in Remote Healthcare.Diagnostics (Basel, Switzerland) · 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
- Next-generation diabetes diagnosis and personalized diet-activity management: A hybrid ensemble paradigm.PloS one · 2025Article
- Article
- The anesthesiologist's guide to critically assessing machine learning research: a narrative review.BMC anesthesiology · 2024Review
- An explainable analysis of diabetes mellitus using statistical and artificial intelligence techniques.BMC medical informatics and decision making · 2024Article
- Robust diabetic prediction using ensemble machine learning models with synthetic minority over-sampling technique.Scientific reports · 2024Article
- An automated approach to predict diabetic patients using KNN imputation and effective data mining techniques.BMC medical research methodology · 2024Article
- An intelligent diabetes classification and perception framework based on ensemble and deep learning method.PeerJ. Computer science · 2024Article
- The role of artificial intelligence in disease prediction: using ensemble model to predict disease mellitus.Frontiers in medicine · 2024Article
- A unified Foot and Mouth Disease dataset for Uganda: evaluating machine learning predictive performance degradation under varying distributions.Frontiers in artificial intelligence · 2024Article
- Improving the Accuracy of Continuous Blood Glucose Measurement Using Personalized Calibration and Machine Learning.Diagnostics (Basel, Switzerland) · 2023Article
- Using Machine Learning for the Risk Factors Classification of Glycemic Control in Type 2 Diabetes Mellitus.Healthcare (Basel, Switzerland) · 2023Article
- Metabolic Syndrome and Overactive Bladder Syndrome May Share Common Pathophysiologies.Biomedicines · 2022Review
- A Hybrid Ensemble Approach for Early-Stage Diabetes Detection.Healthcare technology lettersArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Diabetes is a long-lasting disease triggered by expanded sugar levels in human blood and can affect various organs if left untreated. It contributes to heart disease, kidney issues, damaged nerves, damaged blood vessels, and blindness. Timely disease prediction can save precious lives and enable healthcare advisors to take care of the conditions. Most diabetic patients know little about the risk factors they face before diagnosis. Nowadays, hospitals deploy basic information systems, which generate vast amounts of data that cannot be converted into proper/useful information and cannot be used to support decision making for clinical purposes. There are different automated techniques available for the earlier prediction of disease. Ensemble learning is a data analysis technique that combines multiple techniques into a single optimal predictive system to evaluate bias and variation, and to improve predictions. Diabetes data, which included 17 variables, were gathered from the UCI repository of various datasets. The predictive models used in this study include AdaBoost, Bagging, and Random Forest, to compare the precision, recall, classification accuracy, and F1-score. Finally, the Random Forest Ensemble Method had the best accuracy (97%), whereas the AdaBoost and Bagging algorithms had lower accuracy, precision, recall, and F1-scores.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.