Skip to Main Content
 

Global Search Box

 
 
 
 

Files

File List


Mohammadreza Nemati.pdf (2.3 MB)

ETD Abstract Container

Abstract Header

Machine Learning Approaches in Kidney Transplantation Survival Analysis using Multiple Feature Representations of Donor and Recipient

Nemati, Mohammadreza
http://orcid.org/0000-0002-3505-993X
http://rave.ohiolink.edu/etdc/view?acc_num=toledo1596756241492039

Abstract Details

2020, Master of Science, University of Toledo, Engineering (Computer Science).
Kidney transplantation is the therapy of choice for many people suffering from end-stage renal disease (ESRD). A successful kidney transplant can enhance your standards of living and diminish your risk of dying. Also, people who go through kidney transplantation do not require hours of dialysis treatment on a regular basis. Although this treatment is an optimal treatment of choice, the transplanted kidneys do not work perpetually, and a kidney re-transplantation is required. So, there is a high demand for kidneys, and an ever-increasing number of people have to wait to get kidneys. Consequently, fewer people will wait for a kidney if the average kidney survival times can be increased. One of the critical factors that can impact the survival times is Human Leukocyte Antigen (HLA) matching between donors and recipients. By using machine learning (ML) based predictive survival analysis algorithms, this research carries out an analysis for patients with ESRD by taking into account a novel representation of clinical features to measure the relation between the clinical covariates and graft survival time. The results of four survival algorithms on four feature representations suggest that the gradient boosting (GB) has the highest accuracy in predicting post-transplant kidney survival time. Moreover, comparison of the basic feature representation with other three representations including mismatches, HLA types, and HLA pairs, shows that by incorporating them into the proposed models, they can contribute to enhance the prediction power. Moreover, by preventing a drop in prediction accuracy, the pairs’ obtained information will enable a novel HLA pair analysis method. Furthermore, the results of HLA pair analysis indicate that some HLA pairs can have an advantageous or disadvantageous impact on kidney graft survival time beyond the number of mismatches.
Kevin Xu (Committee Chair)
Stanislaw Stepkowski (Committee Member)
Ahmad Javaid (Committee Member)
83 p.
Computer Science
Graft survival; Survival analysis; Feature extraction

Recommended Citations

Citations

  • Nemati, M. (2020). Machine Learning Approaches in Kidney Transplantation Survival Analysis using Multiple Feature Representations of Donor and Recipient [Master's thesis, University of Toledo]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1596756241492039

    APA Style (7th edition)

  • Nemati, Mohammadreza. Machine Learning Approaches in Kidney Transplantation Survival Analysis using Multiple Feature Representations of Donor and Recipient. 2020. University of Toledo, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=toledo1596756241492039.

    MLA Style (8th edition)

  • Nemati, Mohammadreza. "Machine Learning Approaches in Kidney Transplantation Survival Analysis using Multiple Feature Representations of Donor and Recipient." Master's thesis, University of Toledo, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1596756241492039

    Chicago Manual of Style (17th edition)

toledo1596756241492039
649
© 2020, all rights reserved.
This open access ETD is published by University of Toledo and OhioLINK.