An interpretable classifier for detection of cardiac arrhythmias by using the fuzzy decision tree

Cardiovascular diseases are one of the most wide-sear health problems and the largest cause of mortality in world [1]. Based on the World Health Report 2000, each year the Coronary Artery Disease (CAD) kills an estimated 7 million people, representing 13% of all male deaths and 12% of all female deaths. Thus, low-cost, high-quality cardiac delivery is a critical challenge.

In modern medicine, large amounts of data are generated, but there is a widening gap between data acquisition and data comprehension. It is often impossible to process all of the data available and to make a rational decision on basic trend. Thus, there is a growing presume for intelligent data analysis such as data mining to facilitate the creation of knowledge to support clinicians in making decision. Data mining approaches could be used in such databases, to improve classification task. The role of data mining is to extract important knowledge from large amounts of data, in such a way, that they can be put to use in areas such as decision support and classification.

In this study we chose the fuzzy decision tree, to extract rules decision and realize the classification of some cardiac abnormalities. Decision tree allows easily graphical models and deals with data, having lot of variables without in priori any assumption.

Today, many applications use different tools to extract some interesting information from data, but also to extract knowledge, in order to improve the decision making. Chaing and His [2, 3] have used a fuzzy decision tree as a tool of classification. A fuzzy classifier, fuzzy decision tree has been addressed instead of the other types of classifiers. The method of classification is to predict class labels based on the classification model built by training data.

Many classification methods were used in literature such, Bayesian classifiers [4], decision trees [5], neural [6], rule based learners [7, 8] etc… . A classifier is produced on a set of training instance and a decision is made automatically, on each new instance based upon a forecast of the instance’s classification.

This paper introduces the use of fuzzy decision tree in the analysis of Electrocardiogram (ECG). Section 2 gives the preparation of the database and, deals with the problem of selecting attributes. Section 3 describes briefly a fuzzy decision tree. The fuzzy partition problem is presented in section 4. The classification process and analyses of the fuzzy rules are shown in section 5, Followed by a conclusion.

For full text: click here

(Author: Omar Behadada, M. A Chikh

Published by Sciedu Press)