Prediction Of Long Term Living Donor Kidney Graft Outcome: Comparison Between Different Machine Learning Methods

Maha Fouad; Dr.Mahmoud M. Abd ellatif; Prof.Mohamed Hagag; Dr.Ahmed Akl

doi:10.24297/ijct.v14i2.2066

Authors

Maha Fouad Faculty of Computer and Information,Information System Department,Mansoura University, Mansoura
Dr.Mahmoud M. Abd ellatif Associated Professor, Faculty of Computer and Information, Information System Department,Helwan University
Prof.Mohamed Hagag Vice Dean For Education And Student Affairs,faculty of computer and Information, Computer Science Department,Helwan University
Dr.Ahmed Akl Urology & Nephrology Centre, Mansoura University, Mansoura

DOI:

https://doi.org/10.24297/ijct.v14i2.2066

Keywords:

Data Mining, Machine learning, Kidney Transplanation, Classification, WEKA, Rule Based, Decision Tree

Abstract

Predicting the outcome of a graft transplant with high level of accuracy is a challenging task In medical fields and Data Mining has a great role to answer the challenge. The goal of this study is to compare the performances and features of data mining technique namely Decision Tree , Rule Based Classifiers with Compare to Logistic Regression as a standard statistical data mining method to predict the outcome of kidney transplants over a 5-year horizon. The dataset was compiled from the Urology and Nephrology Center (UNC), Mansoura, Egypt. classifiers were developed using the Weka machine learning software workbench by applying Rule Based Classifiers (RIPPER, DTNB),Decision Tree Classifiers (BF,J48 ) and Logistic Regression. Further from Experimental Results, it has been found that Decision Tree and Rule Based classifiers are providing improved Accuracy and interpretable models compared to other Classifier.