A Deep Learning Approach to Diabetes Diagnosis
_{^{ACIIDS 2024}}

Zeyu Zhang, Khandaker Asif Ahmed, Md Rakibul Hasan, Tom Gedeon, Md Zakir Hossain

Diabetes, resulting from inadequate insulin production or utilization, causes extensive harm to the body. Existing diagnostic methods are often invasive and come with drawbacks, such as cost constraints. Although there are machine learning models like Classwise k Nearest Neighbor (CkNN) and General Regression Neural Network (GRNN), they struggle with imbalanced data and result in underperformance. Leveraging advancements in sensor technology and machine learning, we propose a non-invasive diabetes diagnosis using a Back Propagation Neural Network (BPNN) with batch normalization, incorporating data re-sampling and normalization for class balancing. Our method addresses existing challenges such as limited performance associated with traditional machine learning. Experimental results on three datasets show significant improvements in overall accuracy, sensitivity, and specificity compared to traditional methods. Notably, we achieve accuracies of 89.81% in Pima diabetes dataset, 75.49% in CDC BRFSS2015 dataset, and 95.28% in Mesra Diabetes dataset. This underscores the potential of advanced deep learning models, including Transformers, for robust diabetes diagnosis.

Code

See main.ipynb

Citation

@article{zhang2024deep,
  title={A Deep Learning Approach to Diabetes Diagnosis},
  author={Zhang, Zeyu and Ahmed, Khandaker Asif and Hasan, Md Rakibul and Gedeon, Tom and Hossain, Md Zakir},
  journal={arXiv preprint arXiv:2403.07483},
  year={2024}
}