Predictors of Death in the Liver Transplantation Adult Candidates: An Artificial Neural Networks and Support Vector Machine Hybrid-Based Cohort Study - Kamran Bagheri Lankarani; Behnam Honarvar; Farshad Shafi pour; Morteza Bagherpour; Asma Erjaee; Mohammad Reza Rouhezamin; Mojdeh Khorrami; Saeid Amiri Zadeh Fard; Vahid Seifi; Bita Geramizadeh; Heshmatollah Salahi; Saman Nikeghbalian; et al. (Journal of Biomedical Physics and Engineering, 2022)
Background: Model for end-stage liver disease (MELD) is currently used for liver transplantation (LT) allocation, however, it is not a sufficient criterion. Objective: This current study aims to perform a hybrid neural network analysis of different data, make a decision tree and finally design a decision support system for improving LT prioritization.Material and Methods: In this cohort follow-up-based study, baseline characteristics of 1947 adult patients, who were candidates for LT in Shiraz Organ Transplant Center, Iran, were assessed and followed for two years and those who died before LT due to the end-stage liver disease were considered as dead cases, while others considered as alive cases. A well-organized checklist was filled for each patient. Analysis of the data was performed using artificial neural networks (ANN) and support vector machines (SVM). Finally, a decision tree was illustrated and a user friendly decision support system was designed to assist physicians in LT prioritization. Results: Between all MELD types, MELD-Na was a stronger determinant of LT candidates’ survival. Both ANN and SVM showed that besides MELD-Na, age and ALP (alkaline phosphatase) are the most important factors, resulting in death in LT candidates. It was cleared that MELD-Na <23, age <53 and ALP <257 IU/L were the best predictors of survival in LT candidates. An applicable decision support system was designed in this study using the above three factors.  Conclusion: Therefore, Meld-Na, age and ALP should be used for LT allocation. The presented decision support system in this study will be helpful in LT prioritization by LT allocators.