ArticleBMC bioinformatics2023
An effective correlation-based data modeling framework for automatic diabetes prediction using machine and deep learning techniques.
Article in BMC bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 33 citations in OpenAlex.
- Decision tree with randomized grid search-based hyperparameter tuning and optimal feature scaling for diabetes diagnosis.BMC bioinformatics · 2026Article
- Unveiling the potential of artificial intelligence in revolutionizing disease diagnosis and prediction: a comprehensive review of machine learning and deep learning approaches.European journal of medical research · 2025Review
- Enhanced diabetes prediction using skip-gated recurrent unit with gradient clipping approach.Frontiers in endocrinology · 2025Article
- Unveiling diabetes onset: Optimized XGBoost with Bayesian optimization for enhanced prediction.PloS one · 2025Article
- Validation of Clinical Risk Model to Predict Future Diabetes.Journal of Korean medical science · 2024Article
- Identifying diagnostic indicators for type 2 diabetes mellitus from physical examination using interpretable machine learning approach.Frontiers in endocrinology · 2024Article
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Authors and funding
7 authors at 4 institutions in 3 countries.
Funding
Abstract
The rising risk of diabetes, particularly in emerging countries, highlights the importance of early detection. Manual prediction can be a challenging task, leading to the need for automatic approaches. The major challenge with biomedical datasets is data scarcity. Biomedical data is often difficult to obtain in large quantities, which can limit the ability to train deep learning models effectively. Biomedical data can be noisy and inconsistent, which can make it difficult to train accurate models. To overcome the above-mentioned challenges, this work presents a new framework for data modeling that is based on correlation measures between features and can be used to process data effectively for predicting diabetes. The standard, publicly available Pima Indians Medical Diabetes (PIMA) dataset is utilized to verify the effectiveness of the proposed techniques. Experiments using the PIMA dataset showed that the proposed data modeling method improved the accuracy of machine learning models by an average of 9%, with deep convolutional neural network models achieving an accuracy of 96.13%. Overall, this study demonstrates the effectiveness of the proposed strategy in the early and reliable prediction of diabetes.
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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.