ArticleFrontiers in public health2022
A Decision Support System for Diagnosing Diabetes Using Deep Neural Network.
Article in Frontiers in public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Application Effect of Somatostatin Combined with Transnasal Ileus Catheterization in Patients with Acute Intestinal Obstruction and Advanced Gastric Cancer.Computational intelligence and neuroscience · 2022Trial
- Article
- Statistical and machine-learning assessment of attitudinal, knowledge, and perceptual factors on diabetes awareness in Kuwait.BMC medical informatics and decision making · 2025Article
- Artificial Intelligence-Assisted Clinical Decision-Making: A Perspective on Advancing Personalized Precision Medicine for Elderly Diabetes Patients.Journal of multidisciplinary healthcare · 2025Article
- Diabetes and artificial intelligence beyond the closed loop: a review of the landscape, promise and challenges.Diabetologia · 2024Review
- A deep convolutional neural network for efficient microglia detection.Scientific reports · 2023Article
- Analysis of Diabetes Clinical Data Based on Recurrent Neural Networks.Computational intelligence and neuroscience · 2022Article
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Authors and funding
5 authors.
Funding
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Abstract
Background and Objective: According to the WHO, diabetes mellitus is a long-term condition marked by high blood sugar levels. The consequences might be far-reaching. According to current increases in mortality, diabetes has risen to number 10 among the leading causes of mortality worldwide. When used to predict diabetes using unbalanced datasets from testing, machine learning (ML) classifiers and established approaches for encoding categorical data have exhibited a broad variety of surprising outcomes. Early studies also made use of an artificial neural network to extract features without obtaining a grasp of the sequence information. Methods: This study offers a deep learning-based decision support system (DSS), utilizing bidirectional long/short-term memory (BiLSTM), to accurately predict diabetic illness from patient data. In order to predict diabetes, the BiLSTM hybrid model was used after balancing the data set. Results: Unlike earlier studies, this proposed model's trial findings were promising, with an accuracy of 93.07%, 93% precision, 92% recall, and a 92% F1-score. Conclusions: Using a BILSTM model for classification outperforms current approaches in the diabetes detection domain.
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