ArticleHealthcare (Basel, Switzerland)2021
A Fusion-Based Machine Learning Approach for the Prediction of the Onset of Diabetes.
Article in Healthcare (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- An Effective Model-Based Voting Classifier for Diabetes Mellitus Classification.Bioengineering (Basel, Switzerland) · 2026Article
- A Sophisticated Onscreen Smart Framework for Predicting Diabetes in Remote Healthcare.Diagnostics (Basel, Switzerland) · 2026Article
- A new extended belief rule base method based on neighborhood covering reduction for diabetes diagnosis.PloS one · 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
- Artificial intelligence in financial market prediction: advancements in machine learning for stock price forecasting.Frontiers in artificial intelligence · 2025Review
- Automated sample annotation for diabetes mellitus in healthcare integrated biobanking.Computational and structural biotechnology journal · 2024Article
- Image Enhancement Using Bidimensional Empirical Mode Decomposition and Morphological Operations for Brain Tumor Detection and Classification.Asian Pacific journal of cancer prevention : APJCP · 2024Article
- A stacked ensemble machine learning approach for the prediction of diabetes.Journal of diabetes and metabolic disorders · 2024Article
- Supervised Machine Learning-Based Prediction of Hydrogen Storage Classes Utilizing Dibenzyltoluene as an Organic Carrier.Molecules (Basel, Switzerland) · 2024Article
- Predictive Value of the Total Bilirubin and CA50 Screened Based on Machine Learning for Recurrence of Bladder Cancer Patients.Cancer management and research · 2024Article
- A feature optimization study based on a diabetes risk questionnaire.Frontiers in public health · 2024Article
- Predicting the Onset of Diabetes with Machine Learning Methods.Journal of personalized medicine · 2023Review
- An enhanced diabetes prediction amidst COVID-19 using ensemble models.Frontiers in public health · 2023Article
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Authors and funding
6 authors.
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Abstract
A growing portfolio of research has been reported on the use of machine learning-based architectures and models in the domain of healthcare. The development of data-driven applications and services for the diagnosis and classification of key illness conditions is challenging owing to issues of low volume, low-quality contextual data for the training, and validation of algorithms, which, in turn, compromises the accuracy of the resultant models. Here, a fusion machine learning approach is presented reporting an improvement in the accuracy of the identification of diabetes and the prediction of the onset of critical events for patients with diabetes (PwD). Globally, the cost of treating diabetes, a prevalent chronic illness condition characterized by high levels of sugar in the bloodstream over long periods, is placing severe demands on health providers and the proposed solution has the potential to support an increase in the rates of survival of PwD through informing on the optimum treatment on an individual patient basis. At the core of the proposed architecture is a fusion of machine learning classifiers (Support Vector Machine and Artificial Neural Network). Results indicate a classification accuracy of 94.67%, exceeding the performance of reported machine learning models for diabetes by ~1.8% over the best reported to date.
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