• Tanmayee Parbat
• Rohan Benhal
• HONEY JAIN

Data Mining is a methodology that use a variety of ways to uncover patterns or extract information from databases for use in decision-making and forecasting. In this study, an intelligent and effective method for predicting cardiac illness is examined utilising the Naive Bayes modelling technique. For the web-based application, the user must fill in the relevant values for the attributes. The data is retrieved from a database and is used to link training data to the value entered by the user. Traditional approaches cannot reliably detect cardiac illness, but this research can help clinicians make the best judgments possible. To diagnose heart illness, Naive Bayes is utilised for classification, and this method divides output data into no, low, average, high, and extremely high categories. As a result, two basic functions, categorization and prediction, are carried out. The accuracy of the system is determined by the method and database employed, and the Naive Bayes data categorization technique achieves a 98 percent accuracy.

datamining,

"Novel Model for Predictions in Heart Disease using Techniques of Data Mining", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 10, page no.c262-c267, October-2021, Available :http://www.jetir.org/papers/JETIR2110232.pdf

"Novel Model for Predictions in Heart Disease using Techniques of Data Mining", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 10, page no. ppc262-c267, October-2021, Available at : http://www.jetir.org/papers/JETIR2110232.pdf