Predicting Histological Progression in Primary Biliary Cirrhosis Using Advanced Machine Learning Techniques

Cirrhosis is considered one of the most serious liver diseases worldwide, closely related to excessive alcohol consumption and inadequate eating habits, factors that progressively deteriorate people’s health. In this context, the present research aimed to develop and validate a predictive model based on Machine Learning (ML), specifically using the Random Forest (RF) algorithm, to determine the histological stage (from 1 to 4) in patients with primary biliary cirrhosis. For the development of the study, the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology was applied, which includes the stages of business understanding, data understanding, data preparation, modeling, and evaluation. The results obtained showed that the model presents better performance in the more advanced stages of the disease. The area under the curve (AUC) increased from 0.612 in Stage 1 to 0.874 in Stage 4, reflecting a notable improvement in its discriminative capacity. Similarly, metrics such as sensitivity, precision, and F1-score showed an upward trend, reaching their highest values in Stage 4. In this sense, the proposed model represents a complementary diagnostic support tool, since it allows estimating the histological stage through the analysis of clinical data, contributing to medical decision-making without relying exclusively on invasive procedures.

@article{Andrade-Arenas2026,
  title     = {Predicting Histological Progression in Primary Biliary Cirrhosis Using Advanced Machine Learning Techniques},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {6},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Laberiano Andrade-Arenas and Cesar Yactayo-Arias and Inoc Rubio Paucar},
  doi       = {10.14569/IJACSA.2026.0170695},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170695}
}