WITHDRAWN: An efficient self-tuning spectral clustering algorithm for chronic kidney disease prediction

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Abstract Recently, Chronic Kidney Disease (CKD) is an increasingly severe problem which considered as a global health problem. To control CKD disease, early detection and characterization are considered to be important factors. In literature, many techniques were developed to identify CKD at an early stage but it fails to accurate identification because of redundancy and high dimension data. To solve the abovementioned problems, the efficient data mining technique must be utilized which increases accuracy for the identification of CKD. Hence, in this paper, self-tuning spectral clustering is introduced to reduce the high dimension and redundancy problem in data. The self-tuning spectral clustering is shown to extract and reveal required information from laboratory and clinical patient data which most helpful to assist physicians in increasing the accuracy of CKD identification before reach a severe stage. The clustering results are applying to machine learning techniques such as Deep Neural Network (DNN), Artificial Neural Network (ANN), Support Vector Machine (SVM), Random Forest (RF) and K-Nearest Neighbour (KNN). The proposed technique is implemented in the Phython and performance is evaluated. The performance of the proposed technique is analyzed with statistical measurements such as accuracy, specificity, precision, recall, sensitivity and F_score. In comparison analysis, our finding shows that SVM and KNN provide better identification of CKD with clustering technique and maximum 0.96% accuracy.