A Tabular Residual Neural Network for Diabetes Classification and Prediction
Abstract
Diabetes Mellitus (DM) is a metabolic disorder characterized by hyperglycemia, with type 1 characterized as an autoimmune destruction of pancreatic beta cells and type 2 characterized by insulin resistance with progressive beta cell dysfunction. This study applied an existing binary classification algorithm (ALTARN) to accurately predict DM. ALTARN, as a tabular attention residual neural network, uses residual connection to find complex patterns present in tabular columns. We achieved an average training accuracy of 75.22%. Furthermore, a robust set of validation metrics was obtained via five-fold stratified cross-validation, yielding an average accuracy of 74.61%, an average precision of 72.36%, a mean recall of 79.69%, and a mean F1 score of 75.83%.
Links & Resources
Authors
Cite This Paper
A., H., M., A., K., B. (2025). A Tabular Residual Neural Network for Diabetes Classification and Prediction. arXiv preprint arXiv:10.64898/2025.12.29.25343132.
Hammond, A., Afridi, M., and Balakrishna, K.. "A Tabular Residual Neural Network for Diabetes Classification and Prediction." arXiv preprint arXiv:10.64898/2025.12.29.25343132 (2025).