Total views : 892

Applying Machine Learning Techniques for Predicting the Risk of Chronic Kidney Disease

Affiliations

  • Department of Computer Science and Applications, SRM Arts and Science College, Kattankulathur - 603203, Tamil Nadu, India

Abstract


Objective: This paper aims at predicting the early detection of chronic kidney disease also known as chronic renal disease for diabetic patients with the help of machine learning methods and finally suggests a decision tree to arrive at concrete results with desirable accuracy by measuring its performance to its specification and sensitiveness. Methods: The behaviour of learning algorithms determined on a set of data mining indicators has a proportionate effect on the resulting models. Discovering the knowledge from wide databases is termed as Data mining. Besides studying the existing available Clinic Foundation Heart Disease dataset, 600 clinical records collected by us from a leading Chennai based diabetes research centre. We have tested the dataset for classification using Naïve Bayes and Decision tree method. Findings: On comparing the classification algorithms with respect to Naïve Bayes and Decision tree, we came to conclusion that the accuracy is up to 91% for Decision tree classification. Applications/Improvement: In order to increase the accuracy of the prediction result, we have utilized algorithms such as neural network and clustering data which greatly helped in our mission and also gave scope for future research.

Keywords

Chronic Kidney Disease (CKD), Data Mining, Decision Tree, Diabetes, Naïve Bayes Method.

Full Text:

 |  (PDF views: 832)

References


  • Koh HC, Tan G. Data mining applications in healthcare. Journal of Healthcare Information Management. 2005; 19(2):64–72.
  • Purushotaman G, Krishnakumari P. A survey of data mining techniques on risk prediction: Heart disease. Indian Journal of Science and Technology. 2015 Jun; 8(12):1–5.
  • World Health Organization. Definition and diagnosis of diabetes mellitus and intermediate hyperglycemia. Available form: http://www.who. int/diabetes/en
  • Lakshmi KR, Nagesh Y. Performance comparison of three data mining techniques for predicting kidney dialysis survivability. International Journal of Advances in Engineering and Technology. 2014 Mar; 7(1):242–54.
  • National Kidney Foundation (NKF). The facts about Chronic Kidney Disease (CKD). Available from: https:// www.kidney.org/kidneydisease/aboutckd
  • Jena L, Kamila NK. Distributed data mining classification algorithms for prediction of chronic kidney disease. International Journal of Emerging Research in Management and Technology. 2015 Nov; 4(11):110–8.
  • Vijayarani S, Dhayanand S. Data mining classification algorithms for kidney disease prediction. International Journal on Cybernetics and Informatics. 2015 Aug; 4(4):13–25.
  • Sinha P, Sinha P. Comparative study of chronic kidney disease prediction using KNN and SVM. International Journal of Engineering Research and Technology. 2015 Dec; 4(12):608–12.
  • Han J, Kamber M, Pei J. Data mining: Concepts and techniques. 2nd ed. San Francisco: Morgan Kaufman; 1996.
  • Fayyad U, Piatetsky-Shapiro, Smyth P. From data mining to knowledge discovery in databases. AI Magazine; 1996. p. 37–54.
  • Rapid Miner. Machine learning software getting started. Available from: http:// rapidminer.com/learning/gettingstarted/
  • Naïve bayes classifier based on applying bayes theorem. Available from: http://en.wikipedia.org/wiki/Naive bayesclassifier
  • Naïve bayes classifier. Bayes theorem. Available from: http://en.wikipedia.org/wiki/Naive bayes classifier
  • Sudha A, Gayathri P, Jaisankar N. Effective analysis and predictive model of stroke disease using classification methods. International Journal of Computer Applications. 2012 Apr; 43(14).
  • Decision Tree, C4.5 Algorithm. Machine Learning Algorithm Description. Available from: http://en.wikipedia.org/wiki/C4.5_algorithm

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.