Date of Award
12-2024
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Mathematics and Systems Engineering
First Advisor
Nezamoddin Nezamoddini-Kachouie, Ph.D.
Second Advisor
Andrew Palmer, Ph.D.
Third Advisor
Aaron Welters, Ph.D.
Fourth Advisor
Xianqi Li, Ph.D.
Abstract
Over seven thousand people on average die each year in the United States waiting for an organ transplant due to the shortage of donated organs. With this alarming concern, efforts from the health organizations like the United Network Organ Sharing (UNOS) and government officials have considered avenues to remedy this distress, one of which is to investigate the characteristics among donors and recipients that affects the longevity of donated organs. The goal of this project is to investigate the survival time of transplanted kidneys from 1987 to 2018 with regards to the donors’ and the recipients’ characteristics including gender, ethnicity, blood type, age, BMI, and ischemic time to provide better assertiveness via using artificial intelligence (AI) and advanced mathematical methods.
Survival analysis is performed to determine the associated characteristics with survival time of transplanted kidneys. Our results indicate that there is a noticeable correlation between the survival time and the matching ethnicity of donor and recipient. However, the optimal survival time was not necessarily associated with the matching genders of donor and recipient. It was observed that on average the male to female kidney transplant has longer survival times. In terms of blood types, there were correlations between non-matching blood types associated with longer kidney survival time. The premise of this study was based on an overall generalization among the cohorts in the UNOS registry. We will investigate kidney survival outcome based on donor/recipient attributes, along with features that influence kidney survival time the most.
Our focus is to discuss the results that were obtained from the methods that we have applied based on the data that was provided. We must emphasize that the context of this research is bounded within the domain of statistical analysis and within the scope of the methods that were employed in this study. The outcomes of this study are merely of statistical interest and do not warrant any clinical significance.
Recommended Citation
Despeignes, Alain Edward, "A Machine Learning Approach for Survival Analysis of Transplanted Kidneys based on Donors’ and Recipients’ Factors." (2024). Theses and Dissertations. 1500.
https://repository.fit.edu/etd/1500