Data Mining Techniques Used in Prediction of Heart Diseases

Abstract

Abstract

Heart disease is a big life minatory ailment that cause to demise. It has deep long-term incompetence. There is a huge amount of data available within our system. Nevertheless, we are unable to find hidden relationship and prosperity in data. KDD (knowledge discovery in database) process, data mining consists of various techniques to convert these mounds of data into useful decision-making information. Data mining takes less time for the prediction of the disease as compared to other traditional methods of decision-making. In this study, we compare several papers in which more than one algorithm of data mining are used in the prediction of heart diseases. The important factors composed in dataset are: age, sex, weight, blood pressure, cholesterol, hypertension etc.

Keywords: Data mining, heart diseases, decision tree techniques, Naïve Bayes, neural network

Cite this Article

Saurav Kumar Jha, Rekha Jain. Data Mining Techniques Used in Prediction of Heart Diseases. Journal of Advanced Database Management & Systems. 2019; 6(3): 9–11p.