Risks of Chronic Kidney Disease Prediction using various Data Mining Algorithms

Akalya Devi C; Fatima Abdul Jabbar; Kavi Varshini S; Kriti S Rithanya; Miruthubashini M; Naveena K S

doi:10.34010/injiiscom.v2i2.6907

Akalya Devi C Assistant Professor, 2UG Scholar, Department of Information Technology, PSG College of Technology, Coimbatore, India.
Fatima Abdul Jabbar Assistant Professor, 2UG Scholar, Department of Information Technology, PSG College of Technology, Coimbatore, India.
Kavi Varshini S Assistant Professor, 2UG Scholar, Department of Information Technology, PSG College of Technology, Coimbatore, India.
Kriti S Rithanya Assistant Professor, 2UG Scholar, Department of Information Technology, PSG College of Technology, Coimbatore, India.
Miruthubashini M Assistant Professor, 2UG Scholar, Department of Information Technology, PSG College of Technology, Coimbatore, India.
Naveena K S Assistant Professor, 2UG Scholar, Department of Information Technology, PSG College of Technology, Coimbatore, India.

DOI: https://doi.org/10.34010/injiiscom.v2i2.6907

Keywords: Chronic kidney disease, K-Nearest Neighbor, Classification, Predictive Analytics, Decision Tree, data mining, Support Vector Machine, Random Forest

Abstract

Twenty million people have chronic kidney disease
where patients experience a gradual deterioration of
kidney function, the result of which is kidney failure.
Early detection of chronic renal disease can help to slow
its progression, avert complications, and reduce the risk
of cardiovascular complications. Data mining has been
broadly used in order to support medical professionals
and physicians in the prediction and examination. Here,
in this paper, multiple data mining algorithms are used
to solve a problem in the field of medical diagnosis and
examine how effective they were at predicting the
consequences. The study's focus was on the diagnosis of
chronic renal disease. This dataset used for this study
consists 400 instances & 25 attributes. Preprocessing of
the large amount of raw data is carried out to impute
any missing data and determine which of the variables
should be taken into account in the prediction models.
The accuracy of the prediction is used to compare and
contrast the various predictive analytic models

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