ArticleDiagnostics (Basel, Switzerland)2023
A Novel Proposal for Deep Learning-Based Diabetes Prediction: Converting Clinical Data to Image Data.
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed, 63 citations in OpenAlex.
- An interpretable progressive residual network for automated multiclass diabetes diagnosis.Scientific reports · 2026Article
- Enhanced diabetes prediction using pre-trained CNNs, LSTM, and conditional GAN on transformed numerical data.Scientific reports · 2026Article
- My diabetes care: an AI-based mobile app with conversational agent for type 2 diabetes self-management.Scientific reports · 2025Article
- Research on insider threat detection based on personalized federated learning and behavior log analysis.Scientific reports · 2025Article
- Generalizability of machine learning models for diabetes detection a study with nordic islet transplant and PIMA datasets.Scientific reports · 2025Article
- DiabetesXpertNet: An innovative attention-based CNN for accurate type 2 diabetes prediction.PloS one · 2025Article
- Enhanced diabetes prediction using skip-gated recurrent unit with gradient clipping approach.Frontiers in endocrinology · 2025Article
- Article
- Pediatric diabetes prediction using deep learning.Scientific reports · 2024Article
- A systematic review on diabetic retinopathy detection and classification based on deep learning techniques using fundus images.PeerJ. Computer science · 2024Article
- Revolutionizing Early Disease Detection: A High-Accuracy 4D CNN Model for Type 2 Diabetes Screening in Oman.Bioengineering (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Diabetes, one of the most common diseases worldwide, has become an increasingly global threat to humans in recent years. However, early detection of diabetes greatly inhibits the progression of the disease. This study proposes a new method based on deep learning for the early detection of diabetes. Like many other medical data, the PIMA dataset used in the study contains only numerical values. In this sense, the application of popular convolutional neural network (CNN) models to such data are limited. This study converts numerical data into images based on the feature importance to use the robust representation of CNN models in early diabetes diagnosis. Three different classification strategies are then applied to the resulting diabetes image data. In the first, diabetes images are fed into the ResNet18 and ResNet50 CNN models. In the second, deep features of the ResNet models are fused and classified with support vector machines (SVM). In the last approach, the selected fusion features are classified by SVM. The results demonstrate the robustness of diabetes images in the early diagnosis of diabetes.
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