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Data Mining Techniques Used To Predict Chronic Kidney Disease

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  • Chithra A G
  • Chandana B
  • Darshan R
  • Harshitha H S
  • Nasreen Fathima

Abstract

Chronic kidney disease is a global health issue and area of concern, associated with an increased risk of cardio vascular diseases and chronic renal failure[1]. It is a symptom where kidney fails to filter toxic wastes from the body, which results in decomposition of wastes in human body and leads to dangerous results. The two main causes of this disease are diabetes and high blood pressure, which are responsible for up to two-third of cause[5]. The healthcare sector has huge medical data but the main difficulty is how to cultivate the existing information into useful practices[3]. To unfold this hurdle the concept of data mining is best suited. The main objective of this paper is to use data mining technique such as random forest, RBF, K-means clustering and Naïve Bayes for the prediction of chronic kidney disease and to summarize the efficiency of Naïve Bayes method by generating suitable results.

Suggested Citation

  • Chithra A G & Chandana B & Darshan R & Harshitha H S & Nasreen Fathima, 2018. "Data Mining Techniques Used To Predict Chronic Kidney Disease," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 4(6), pages 54-58, May.
    • Handle: RePEc:jbh:ijsrcs:v4:y2018:i6:id:hcseit184612
    Note: Article URL: https://ijsrcseit.com/CSEIT184612
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