الفهرس 
IntroductionOnly 14 pages are availabe for public view
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Abstract
Kidney transplantation outcome prediction is very significant and doesn’t require emphasis. This will grant the selection of the best available kidney donor and the best immunosuppressive treatment for patients. Survival prediction before treatment could simplify patient’s decision making and boost survival by altering clinical practice. The exact prediction of kidney transplantation outcome is still not accurate even with the enhancements in acute rejection results. Machine learning methods introduce many ways to solve the kidney transplantation prediction problem than that of other methods. The power of any prediction method relies on the choosing of the proper variables. Feature selection is one of the important preprocessing procedures. It is the method that selects the minimal suitable variables that introduced in a set of features. In this study, two proposed prediction methods and a proposed feature selection method for kidney transplantation prediction are introduced to classify graft status result.
The first proposed prediction method is based on data mining techniques to predict five-year graft survival after transplantation. This method composes of three stages: Data Preparation Stage (DPS), Feature selection Stage (FSS), and Prediction Stage (PS). The proposed prediction method merges information gain with naïve bayes and k-nearest neighbor. Initially, it uses information gain to select the essential features, uses naïve bayes to select the most essential features. These two methods are combined
Abstract
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in a hybrid feature selection method which chooses the minimum number of features that produce highest accuracy. Finally, it uses k-nearest neighbor for graft survival prediction classification. The proposed prediction method has been evaluated against recent techniques. Experimental results have proven that the proposed prediction method outperforms the recent techniques as it attains the maximum accuracy and f-measure with minimal errors. This prediction method can also be used in other transplant datasets.
Additionally, the second prediction method presents an integrated prediction method, an intelligent feature selection method, and a modified k-nearest neighbor. Choosing the proper features is accomplished by merging three feature selectors. The proposed feature selection method is accomplished using gain ratio, naïve bayes, and genetic algorithm. Next, the cleaned dataset is utilized to provide quick and precise outcome throughout a modified k-nearest neighbor classifier. Each stage of this proposed method has been evaluated using intense experiments. Experimental results demonstrate the efficiency of all the steps of the proposed method. Moreover, the proposed method has been evaluated versus latest methods. The results presented that this method outperformed all latest and similar literature methods. This method can as well be employed to other related transplant datasets.
Also, a proposed feature selection method that combines statistical methods with classification procedures of data mining technology to predict the probability of graft survival after kidney transplantation was introduced. Univariate analysis using kaplan-meier survival analysis method combined with naïve bayes classifier was used to specify the significant variables. Three data mining tools, namely naïve bayes, decision tree and k-nearest neighbor
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classifiers were utilized to examine the instances of kidney transplantation, and their accuracy was compared with using the proposed feature selection method and without using it. Experimental results have presented that the proposed feature selection method have better results than other techniques.