Intelligent feature selection with modified K-nearest neighbor for kidney transplantation prediction

The prediction of kidney transplantation outcome is an important challenge and does not need emphasis because of the lack of available organs. Graft survival prediction is significant to help physicians to take the right decision and enhance survival rate by changing medical procedure. Also, it helps in the best choice of the existing kidney donor and the immunosuppressive management suitable for a patient. But the exact prediction of the graft survival is still not accurate despite of the advancements in this field. The purpose of our research is to design an intelligent kidney transplantation prediction method to solve the prediction problem by utilizing data mining methods.The novelty of this study is focused in presenting: (a) an integrated prediction method, (b) a new intelligent feature selection method, and (c) a modified K-nearest neighbor. Choosing the proper variables is accomplished by merging three feature selectors. The new proposed feature selection method is accomplished using gain ratio, naive 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. Additionally, 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.