Prediction of Chronic Kidney Disease Statistics Using Data Mining Techniques
Institute of Scholars (InSc), 2020
10 Pages Posted: 21 Sep 2020
Date Written: 2020
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
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