Predicting kidney transplantation outcome based on hybrid feature selection and KNN c

Predicting kidney transplantation outcome based on hybrid feature selection and KNN classifier
Authors
Dalia M Atallah, Mohammed Badawy, Ayman El-Sayed, Mohamed A Ghoneim
Publication date
2019/7/30
Journal
Multimedia Tools and Applications
Volume
78
Issue
14
Pages
20383-20407
Publisher
Springer US
Description
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. This paper proposes a new novel prediction method based on data mining techniques to predict five-year graft survival after transplantation. This new proposed prediction method composes of three stages: data preparation stage (DPS), feature selection stage (FSS), and prediction stage (PS). The new 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 in a new hybrid feature …

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