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Chronic Kidney Disease Using Fuzzy C-Means Clustering Analysis

Author

Listed:
  • Vineeta Kunwar

    (Amity University Uttar Pradesh, Noida, India)

  • A. Sai Sabitha

    (Amity University Uttar Pradesh, Noida, India)

  • Tanupriya Choudhury

    (Informatics Department, School of CS, University of Petroleum and Energy Studies, Dehradun, India)

  • Archit Aggarwal

    (Amity University Uttar Pradesh, Noida, India)

Abstract

Medical industries are encountered with challenges like providing quality services to patients, correct diagnosis and effective treatments at reasonable cost. Data mining has become a necessity and provides solutions to many important and critical health related concerns. It is the process to mine knowledgeable information from voluminous medical data sets. It plays an essential role in improving medical decision making and helps to investigate trends in patient conditions which can be used by doctors for disease diagnosis. Clustering is an unsupervised learning technique that groups object with high similarity together. Chronic kidney disease (CKD) causes renal failure and kidney dysfunction. It has become an important health issue with the number of cases on the rise every year. This article presents analysis and detection of Chronic Kidney Disease using Fuzzy C Means (FCM) clustering which is effective in mining complex data having fuzzy relationships among members. FCM will investigate and group together the patients having CKD and Not CKD. The simulation and coding are done in MATLAB.

Suggested Citation

  • Vineeta Kunwar & A. Sai Sabitha & Tanupriya Choudhury & Archit Aggarwal, 2019. "Chronic Kidney Disease Using Fuzzy C-Means Clustering Analysis," International Journal of Business Analytics (IJBAN), IGI Global, vol. 6(3), pages 43-64, July.
    • Handle: RePEc:igg:jban00:v:6:y:2019:i:3:p:43-64
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