A Novel Deep Learning Model for Diabetes Diagnosis
Haniyeh FATTAHI1*, Yousef FARHANG2
1Vocational School, Department of Computer Technologies, Istanbul Esenyurt University, Istanbul, Turkiye
2Faculty of Engineering and Architecture, Department of Computer Engineering, Istanbul Esenyurt University, Istanbul, Turkiye
* Corresponding author: Haniyehfattahi@esenyurt.edu.tr
Presented at the International Symposium on AI-Driven Engineering Systems (ISADES2025), Tokat, Turkiye, Jun 19, 2025
SETSCI Conference Proceedings, 2025, 22, Page (s): 124-127 , https://doi.org/10.36287/setsci.22.1.001
Published Date: 10 July 2025
Diabetes is a chronic disease affecting millions worldwide, necessitating early and accurate diagnosis for effective management. Traditional diagnostic methods, while reliable, often struggle with efficiency and early-stage detection. Deep learning has emerged as a powerful tool in medical diagnostics, offering improved accuracy and predictive capabilities. This paper introduces a novel deep learning model for diabetes diagnosis, leveraging advanced neural network architectures. Trained and evaluated on standard datasets, the model outperforms conventional methods, demonstrating superior diagnostic accuracy and reliability. Our findings underscore the potential of deep learning to enhance diabetes detection and patient outcomes.
Keywords - Deep Learning; Diabetes; Diagnosis; Artificial Intelligence (AI)
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