Predicting Graft Failure Within Year After Transplantation Using Data Mining Techniques

The complex factors of liver transplant survival and the potential for post-transplant complications are significant challenges for healthcare professionals. This paper aims to identify the ability to use data mining techniques to develop a predictive model for liver transplant failure by identifying the relationship between abnormalities in periodic patients' laboratory results and graft failure. The researchers obtained data from King Faisal Specialist Hospital and Research Centre to address the research problems. First, the classification technique was used to predict cases with a high risk of liver transplant failure. Second, Association Rules were applied to identify associations between abnormalities in patients’ laboratory results and transplant failure. Before using data mining algorithms, the patient dataset underwent a cleaning process, which involved removing duplicate entries and uncertain results. The algorithms were applied separately to the data of patients who completed the first year without complications and those who experienced transplant failure. The obtained results were then compared and we observed that abnormal levels in Aspartate Transferase (AST), Red Blood Cell (RBC), Hemoglobin (Hgb), 'Bilirubin Total', and 'Platelet' occurred exclusively in cases that faced liver transplant failure within the first year. Similarly, abnormal levels in 'AST', 'RBC', Alanine Aminotransferase (ALT), and 'Bilirubin Total' were also associated with transplant failure.

@article{Alwazae2024,
  title     = {Predicting Graft Failure Within Year After Transplantation Using Data Mining Techniques},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {11},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Meshari Alwazae and Saad Alghamdi and Lulu Alobaid and Bader Aljaber and Reem Altwaim},
  doi       = {10.14569/IJACSA.2024.0151177},
  url       = {https://doi.org/10.14569/IJACSA.2024.0151177}
}