ReviewVisual computing for industry, biomedicine, and art2021
A comprehensive review of machine learning techniques on diabetes detection.
Review in Visual computing for industry, biomedicine, and art, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.
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, 2 syntheses or guidelines pooled it, 100 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
- Artificial intelligence based prediction models for individuals at risk of multiple diabetic complications: A systematic review of the literature.Journal of nursing management · 2022Pooled it
- A data-driven machine learning model for effective diabetes diagnosis.Scientific reports · 2026Article
- Explainable ensemble machine learning for predicting diabetes mellitus and identifying key risk factors: a population-based study in northern Bangladesh.Scientific reports · 2026Article
- An Effective Model-Based Voting Classifier for Diabetes Mellitus Classification.Bioengineering (Basel, Switzerland) · 2026Article
- Prediction of Diabetes Among Homeless Adults Using Artificial Intelligence: Suggested Recommendations.Healthcare (Basel, Switzerland) · 2026Article
- A Biomarker-Driven and Interpretable Machine Learning Model for Diagnosing Diabetes Mellitus.Food science & nutrition · 2025Article
- Machine Learning Approach to Metabolomic Data Predicts Type 2 Diabetes Mellitus Incidence.International journal of molecular sciences · 2024Observational
- Assessing risk factors for malnutrition among women in Bangladesh and forecasting malnutrition using machine learning approaches.BMC nutrition · 2024Article
- Prediction of Diabetes Using Data Mining and Machine Learning Algorithms: A Cross-Sectional Study.Healthcare informatics research · 2024Article
- A Federated Learning-Inspired Evolutionary Algorithm: Application to Glucose Prediction.Sensors (Basel, Switzerland) · 2023Article
- Environmental exposures in machine learning and data mining approaches to diabetes etiology: A scoping review.Artificial intelligence in medicine · 2023Article
- Application of machine learning methods for the prediction of true fasting status in patients performing blood tests.Scientific reports · 2022Article
- Development and validation of a machine learning-augmented algorithm for diabetes screening in community and primary care settings: A population-based study.Frontiers in endocrinology · 2022Article
- Causal Structural Covariance Network Suggesting Structural Alterations Progression in Type 2 Diabetes Patients.Frontiers in human neuroscience · 2022Article
- Need for an Artificial Intelligence-based Diabetes Care Management System in India and the United States.Health services research and managerial epidemiologyArticle
- Role of Artificial Intelligence in Diabetes Mellitus Care: A SWOT Analysis.Indian journal of endocrinology and metabolismReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Diabetes mellitus has been an increasing concern owing to its high morbidity, and the average age of individual affected by of individual affected by this disease has now decreased to mid-twenties. Given the high prevalence, it is necessary to address with this problem effectively. Many researchers and doctors have now developed detection techniques based on artificial intelligence to better approach problems that are missed due to human errors. Data mining techniques with algorithms such as - density-based spatial clustering of applications with noise and ordering points to identify the cluster structure, the use of machine vision systems to learn data on facial images, gain better features for model training, and diagnosis via presentation of iridocyclitis for detection of the disease through iris patterns have been deployed by various practitioners. Machine learning classifiers such as support vector machines, logistic regression, and decision trees, have been comparative discussed various authors. Deep learning models such as artificial neural networks and recurrent neural networks have been considered, with primary focus on long short-term memory and convolutional neural network architectures in comparison with other machine learning models. Various parameters such as the root-mean-square error, mean absolute errors, area under curves, and graphs with varying criteria are commonly used. In this study, challenges pertaining to data inadequacy and model deployment are discussed. The future scope of such methods has also been discussed, and new methods are expected to enhance the performance of existing models, allowing them to attain greater insight into the conditions on which the prevalence of the disease depends.
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.