A literature Review on Prediction of Kidney Stone Disease (Nephrolithiasis) Using Data Mining Techniques

  • Sankar P a Assistant Professor, Sri Vidya Mandir Arts & Science College, Katteri, Uthangarai, Tamil Nadu -636 902. India.
  • Karthikeyan M Assistant Professor, PG and Research Department of Computer Science, Government Arts College (Autonomous), Salem, Tamil Nadu-636 007, India.
Keywords: Dm-Data Mining, Ksd-Kidney Stone Disease, Dmt-Data Mining Techniques, Knn-K-Nearest Neighbor, Svm- Support Vector Machine, Ann- Artificial Neural Network


Currently Kidney Stone Disease (Nephrolithiasis) is a rising problem in the world. Due to the high possibility of silent cause of renal failure within a short period of
time, a patient must be hospitalized and properly cured. Several Data Mining techniques are used in the healthcare for predicting the Kidney Stone disease. The Data Mining techniques namely Support Vector Machine (SVM), Naive Bayes, K-Nearest Neighbour (KNN), Decision Tree, Classification and Artificial Neural Network (ANN) are used to analyse the accuracy for the Kidney Stone Disease (KSD).