Predictions in heart disease using techniques of data mining

TLDR

Medical institutions generate vast amounts of data that remain largely untapped, leaving a gap between data abundance and actionable knowledge. The authors propose using data mining methods to uncover patterns for heart disease prediction. They evaluate Naive Bayes, neural networks, and decision tree algorithms on medical datasets to extract predictive knowledge.

Abstract

As huge amount of information is produced in medical associations (healing facilities, therapeutic focuses) yet this information is not properly utilized. The health care system is "data rich" however "knowledge poor ". There is an absence of successful analysis methods to find connections and patterns in health care data. Data mining methods can help as remedy in this circumstance. For this reason, different data mining techniques can be utilized. The paper intends to give details about various techniques of knowledge abstraction by using data mining methods that are being used in today's research for prediction of heart disease. In this paper, data mining methods namely, Naive Bayes, Neural network, Decision tree algorithm are analyzed on medical data sets using algorithms.