Ordinal evolutionary artificial neural networks for predicting diabetic nephropathy progression
Antonio Manuel Gómez-Orellana 
Rafael Ayllón-Gavilán 
Víctor Manuel Vargas-Yun 
Pedro Antonio Gutiérrez 
César Hervás-Martínez 
Luca Romeo Applied Soft Computing
COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Abstract
Diabetic Nephropathy (DN) is a complex, multi-factorial condition that often coexists with other diabetes-related comorbidities. Although DN progresses through a series of ordered stages, i.e., from mild to advanced, studies have primarily focused on classifying its presence or absence, i.e., a static risk prediction, rather than its progression. This gap underscores the need for advanced methodologies to predict DN progression, which could improve patient outcomes and optimise healthcare interventions. This study proposes a novel ordinal perspective to predicting DN progression using clinical Electronic Health Records (EHR) data. This approach is based on Ordinal Evolutionary Artificial Neural Networks (OEANNs), which integrate a Cumulative Link Model to perform ordinal predictions, and leverage Evolutionary Algorithms to optimise OEANNs architecture and weights by dynamically adapting to the sparsity of EHR data. The proposed ordinal perspective involves discretising the disease risk into four ordinal severity classes, each representing a different stage of disease progression. In addition, the temporal variability of DN progression is modelled by considering a feature engineering stage that constructs variables capturing early indicators of DN. Experimental results on a clinical EHR dataset demonstrate that OEANNs outperform state-of-the-art nominal and ordinal models, achieving significant improvements in ordinal metrics such as Mean Absolute Error and Quadratic Weighted Kappa. Furthermore, unlike traditional static risk prediction, OEANNs minimise misclassification errors between distant severity stages, enabling more accurate predictions of disease severity. Therefore, the proposed innovative ordinal approach bridges a critical gap in DN management, offering robust, clinically relevant predictions to inform personalised treatment planning.
Keywords
BibTex Citation
@article{Gomez2026Ordinal,
author = {G{\' o}mez-Orellana, Antonio Manuel and Bernardini, Michele and Ayll{\' o}n-Gavil{\' a}n, Rafael and Vargas-Yun, V{\' i}ctor Manuel and Guti{\' e}rrez, Pedro Antonio and Herv{\' a}s-Mart{\' i}nez, C{\' e}sar and Romeo, Luca},
journal = {Applied Soft Computing},
doi = {10.1016/j.asoc.2026.115153},
year = {2026},
pages = {115153},
title = {Ordinal evolutionary artificial neural networks for predicting diabetic nephropathy progression},
url = {https://www.sciencedirect.com/science/article/pii/S1568494626006010},
howpublished = {https://www.sciencedirect.com/science/article/pii/S1568494626006010},
volume = {197},
}
BibTex Unicode Citation
@article{Gomez2026Ordinal,
author = {Gómez-Orellana, Antonio Manuel and Bernardini, Michele and Ayllón-Gavilán, Rafael and Vargas-Yun, Víctor Manuel and Gutiérrez, Pedro Antonio and Hervás-Martínez, César and Romeo, Luca},
journal = {Applied Soft Computing},
doi = {10.1016/j.asoc.2026.115153},
year = {2026},
pages = {115153},
title = {Ordinal evolutionary artificial neural networks for predicting diabetic nephropathy progression},
url = {https://www.sciencedirect.com/science/article/pii/S1568494626006010},
howpublished = {https://www.sciencedirect.com/science/article/pii/S1568494626006010},
volume = {197},
}
APA Citation
Gómez-Orellana, A. M., Bernardini, M., Ayllón-Gavilán, R., Vargas-Yun, V. M., Gutiérrez, P. A., Hervás-Martínez, C., & Romeo, L. (2026). Ordinal evolutionary artificial neural networks for predicting diabetic nephropathy progression. Applied Soft Computing, 197, 115153. https://doi.org/10.1016/j.asoc.2026.115153
RIS Citation
TY - JOUR
AU - Gómez-Orellana, Antonio Manuel
AU - Bernardini, Michele
AU - Ayllón-Gavilán, Rafael
AU - Vargas-Yun, Víctor Manuel
AU - Gutiérrez, Pedro Antonio
AU - Hervás-Martínez, César
AU - Romeo, Luca
DA - 2026///
PY - 2026
DO - 10.1016/j.asoc.2026.115153
ID - temp_id_312152424217
SP - 115153
T2 - Applied Soft Computing
TI - Ordinal evolutionary artificial neural networks for predicting diabeti
c nephropathy progression
UR - https://www.sciencedirect.com/science/article/pii/S1568494626006010
VL - 197
ER -
CV Citation
A.M. Gómez-Orellana, M. Bernardini, R. Ayllón-Gavilán, V.M. Vargas-Yun (CA), P.A. Gutiérrez, C. Hervás-Martínez, L. Romeo (4/7). "Ordinal evolutionary artificial neural networks for predicting diabetic nephropathy progression". Applied Soft Computing, Vol. 197, pp. 115153, 2026. (Q1, IF: 6.6).