A Survey on Various Data Mining Techniques for ECG Meta Analysis

📝 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.

💡 Analysis

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.

📄 Content

A Survey On Various Data Mining Techniques for
ECG Meta Analysis Kratika Tyagi Sanjeev Thakur

        Department of CSE,
                                                                           Department of CSE,

    ASET, Amity University
      
     
                                              ASET, Amity University
   
   NOIDA, Uttar Pradesh, India
                  
                                            NOIDA, Uttar Pradesh, India
 Email ID: kratika.tyagi@student.amity.edu                                                                     Email ID: sthakur3@amity.edu

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. Keywords: Data Mining, Data Mining Techniques- Classification, Clustering, Regression, ECG. I. INTRODUCTION 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. The objective of data mining procedure is to concentrate data from a dataset and change it into a justifiable structure for further utilization. There are various data mining techniques which can be applied in various fields of technology. By using data mining techniques, it is observed that less time is taken for the prediction of any disease with more accuracy. Electrocardiography (ECG) is the procedure of recording the electrical activity of the heart over a timeframe using electrodes set on a patient’s body. These electrodes recognize the minor electrical changes on the skin that rise up out of the heart muscle depolarizing in the midst of each heartbeat. Biomedical applications have played an imperative role in the enhancement of medical diagnosis and have provided many solutions for the identification of diseases. The primary aim of such applications is to help doctors and medical practitioners make effective decisions by analyzing various computer generated reports. However in most cases it has been observed that there is a huge difference in what is interpreted and what is enacted. Using data mining techniques we aim to minimize the error of making effective decisions by categorizing various Interpretations of ECG data. This paper will have a proper literature survey of the various data mining techniques over the ECG data to find the most suitable classifiers and clustering algorithms. This will be followed by developing a data mining model and compare it with the most suitable data mining algorithm over ECG data. II. TECHNIQUES USED IN DATA MINING A. Association Association is considered as the best data mining strategy. In this mining strategy, pattern is considered on the basis of a connection of a specific thing or assortment of things in the same exchange. Association method is generally utilized in the prediction of the heart diseases as it let us know the relationship among different set of attributes used in analysis. B. Clustering Clustering is another data mining techniques which makes clusters of objects which are similar in characteristics. Clustering basically defines classes and put objects in them. Example- By using clustering technique to predict the heart disease we get clusters or we can make the list of those patients which are having the same risk factor and separate list for those patients which are having high blood pressure. C. Classification Classification is a data mining strategy which depends on machine learning. Classification is basically utilized to classify things in a specific arrangement of information into one of predefined set of classes or groups. It uses numerical methods like decision trees, neural networks et

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