• SANSKRITI PANDEY
• DIVESH GUPTA
• PRANIT OZA

Analytics based on predictions for healthcare with the use of data-mining is challenging job in helping doctors evaluate the appropriate treatments for retaining lives. In this project, we present data-mining methods for prediction of the chronic kidney disease by use of clinical data and choose the one having highest accuracy while providing interface for user. Different data-mining techniques are evaluated including Neural Network (NN), support vector machine (SVM) and Random Forest (RF). These models that are predictive in nature have been constructed earlier using chronic renal disease dataset and their performances have been compared in order to choose the best classifier for the prediction of the chronic disease. The classifier having the best efficiency is selected after various comparisons. It is implemented and a user interface is provided for the user for entering their medical data. Output predicting the occurrence of chronic kidney disease is obtained.

analytics, classifier, user-interface

"To improve Data Mining based Human Disease Detection System", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 4, page no.258-261, April-2019, Available :http://www.jetir.org/papers/JETIR1904A40.pdf

"To improve Data Mining based Human Disease Detection System", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 4, page no. pp258-261, April-2019, Available at : http://www.jetir.org/papers/JETIR1904A40.pdf