A Survey on Various Data Mining Techniques for ECG Meta Analysis

TL;DR

This paper reviews various data mining techniques such as classification, regression, and clustering applied to ECG data to identify the most effective method for improving diagnostic accuracy and decision-making in medical analysis.

Contribution

It provides a comparative analysis of different data mining techniques on ECG data to determine the most suitable approach for accurate disease prediction.

Findings

Certain data mining techniques outperform others in ECG analysis

Improved accuracy in disease prediction using specific algorithms

Guidelines for selecting data mining methods for ECG data

Abstract

Data Mining is the process of examining the information from different point of view and compressing it for the relevant data. This data can also be utilized to build the incomes. Data Mining is also known as Data or Knowledge Discovery. The basic purpose of data mining is to search patterns which have minimal user inputs and efforts. Data Mining plays a very crucial role in the various fields. There are various data mining procedures which can be connected in different fields of innovation. By using data mining techniques, it is observed that less time is taken for the prediction of any disease with more accuracy. In this paper we would review various data mining techniques which are categorized under classification, regression and clustering and apply these algorithms over an ECG dataset. The purpose of this work is to determine the most suitable data mining technique and use it to improve the accuracy of analyzing ECG data for better decision making.