Prediction of Chronic Kidney Disease Statistics Using Data Mining Techniques

Institute of Scholars (InSc), 2020

10 Pages Posted: 21 Sep 2020

See all articles by Rajesh S. Walse

Rajesh S. Walse

Swami Ramanand Teerth Marathwada University

Aniket Muley

affiliation not provided to SSRN

Dr. Gajanan Kurundkar

SRT MU, Nanded-Maharashtra-India

Parag Bhalchandra

SRT Marathwada University, Nanded

Date Written: 2020

Abstract

In this research, the Apriori associator in the WEKA data mining tool used for pre-processing, exploring, and analyzing the chronic kidney-related data. The minimum matrix or confidence value in the association rule mining supporter, confidence number of cycles performed the role of preparation of Rules. This research is carried out by formatting and found ten best rules. The rules create x belongs to y attributes; the constant output of Apriori is to set the best standards by using its value and over caste, and its production shows the rules in the form of the model. At the time of execution, minimum support is 0.2 (80 instances), and the minimum metric.

Keywords: Data Mining; Classification; Preprocess; Association; Apriori; WEKA; CKD

Suggested Citation

Walse, Rajesh S. and Muley, Aniket and Kurundkar, Dr. Gajanan and Bhalchandra, Parag, Prediction of Chronic Kidney Disease Statistics Using Data Mining Techniques (2020). Institute of Scholars (InSc), 2020, Available at SSRN: https://ssrn.com/abstract=3668753

Rajesh S. Walse (Contact Author)

Swami Ramanand Teerth Marathwada University ( email )

Aniket Muley

affiliation not provided to SSRN

Dr. Gajanan Kurundkar

SRT MU, Nanded-Maharashtra-India ( email )

Parag Bhalchandra

SRT Marathwada University, Nanded ( email )

Nanded, Maharashtra 431605
India

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