Comparison of neural network architectures for diabetes prediction

Guerreiro, Nathan Antonio; Nijo, Rui; Teixeira, João Paulo

http://hdl.handle.net/10198/34624

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Authors

Guerreiro, Nathan Antonio

Nijo, Rui

Teixeira, João Paulo

Abstract(s)

Diabetes represents a significant global health challenge, with millions of individuals affected and substantial impacts on healthcare systems. In this study, we compare two neural network architectures for diabetes prediction: the Feedforward Neural Network (FFNN) and the Cascade-Forward Backpropagation Neural Network (CFBPNN). Utilizing the Diabetes Prediction Dataset, comprising 100,000 samples, and after a balanced result, 17,000 samples were obtained. The networks are trained using the Levenberg-Marquardt and Resilient Backpropagation algorithms, and performance metrics, including precision, sensitivity, specificity, accuracy, F1-score, and computational time, are evaluated. Results indicate that the FFNN architecture paired with the Levenberg-Marquardt algorithm demonstrates superior diagnostic prediction accuracy with 91,10%. However, this comes at the cost of longer computational time compared to the CFBPNN.

Keywords

Neural Network Architectures Diabetes Prediction Machine Learning

URI

http://hdl.handle.net/10198/34624

Citation

Guerreiro, Nathan Antonio; Nijo, Rui; Teixeira, João Paulo.(2025). Comparison of neural network architectures for diabetes prediction. Procedia Computer Science. ISSN 1877-0509. 256, p. 942-948