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