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Optimizing Paired Kidney Transplant by Applying Machine Learning

Jha, Prakash Teknarayan
http://rave.ohiolink.edu/etdc/view?acc_num=toledo1301028157

Abstract Details

2011, Master of Science in Engineering, University of Toledo, College of Engineering.
In this research a tree-based machine learning algorithm has been used to build a robust mechanism that optimizes paired kidney transplants in a very proficient manner. The system predicts how good or bad a specific kidney transplant match is. The system successfully classifies and predicts donor-quality. Potential donors were classified into categories based on numeric attributes generated. Based on these numeric attributes, potential donors were classified into linguistic variables such as Good, Average and Bad. To choose the data mining technique to be employed by the system, several algorithms such as J48 (tree-based machine learning algorithm), JRip (rule-based machine learning algorithm) and SMO (Sequential minimal optimization) were considered. It was found that J48 was the better choice of all the three. The donor-classification was based on donor parameters such as age, HLA A, HLA B, HLA DR, center, CMV, EBV and blood group. The system is built using JAVA, SQL and Weka (a machine learning suite). The system also provides a visual mode of communication for doctors and surgeons to consider key factors like donor quality and donor blood group before carrying out a transplant. This feature also facilitates the doctors’ decision-making, as to where should a chain of transplants be broken so as to ensure better and desired results as well as to provide more leads to feasible transplant chains in the future. The system developed has an accuracy of 97.18% which was generated by correctly classified instances by J48 and a promising 0.9567 kappa statistic achieved by J48. This statistic is a measure to assess the decision making capability of the system built in comparison to a real physician. The predicted donor quality was then incorporated into a matching system where possible matches were visually displayed as optimal matches and other top matches. The optimal matches also showed every donor’s blood group and donor quality, which are important in making transplant decisions.
Devinder Kaur, PhD (Committee Chair)
Mansoor Alam, PhD (Committee Member)
Henry Ledgard, PhD (Committee Member)
77 p.
Artificial Intelligence; Computer Science; Technology
paired kidney transplant; kidney; transplant; prakash; jha; teknarayan; university of toledo; thesis; rees; kaur; selman; ut; paired; optimization; algorithm; machine learning; prakash teknarayan jha; computer science; ms; masters

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Citations

  • Jha, P. T. (2011). Optimizing Paired Kidney Transplant by Applying Machine Learning [Master's thesis, University of Toledo]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1301028157

    APA Style (7th edition)

  • Jha, Prakash. Optimizing Paired Kidney Transplant by Applying Machine Learning. 2011. University of Toledo, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=toledo1301028157.

    MLA Style (8th edition)

  • Jha, Prakash. "Optimizing Paired Kidney Transplant by Applying Machine Learning." Master's thesis, University of Toledo, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1301028157

    Chicago Manual of Style (17th edition)

toledo1301028157
711
© 2011, all rights reserved.
This open access ETD is published by University of Toledo and OhioLINK.