Hybrid Classifier for Identification of Heart Disease

Y. Sharma, Rikku Veliyambara, R. Shettar
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引用次数: 2

Abstract

Cardiovascular disease has increased rapidly in the past few decades. It has become a leading cause of death globally. Heart disease has affected the global heterogeneous population irrespective of age and gender. According to World Health Organization, an estimated 17.3 million people died from cardiovascular diseases in 2008, representing 30% of all global deaths. The accurate and timely prediction of these diseases has become a challenge for medical organizations. A mere assumption of absence or presence of disease is an approach used by many hospitals to give prediction results. The predictions of the heart disease are dependent mainly on the prominent factors involved and their effect weightage. Finding out the patterns and extracting knowledge from those patterns is the major task at hand. Data mining techniques have proven to be a good means for this knowledge discovery. This study makes use of the prominent features of two data mining techniques, namely, K-Means Clustering and Decision Tree. These methods, one being unsupervised learning and the other supervised learning, use very different approaches to predict the results. The positive factors of both the techniques have been used to build a Hybrid Classifier. The aim is to provide an algorithm which gives the best accuracy and performance for the Heart disease identification system.
心脏病识别的混合分类器
在过去的几十年里,心血管疾病迅速增加。它已成为全球死亡的主要原因。心脏病影响着全球不同年龄和性别的异质人群。据世界卫生组织统计,2008年估计有1730万人死于心血管疾病,占全球总死亡人数的30%。准确、及时地预测这些疾病已成为医疗机构面临的挑战。仅仅假设疾病的存在或不存在是许多医院用来给出预测结果的方法。心脏病的预测主要取决于所涉及的突出因素及其影响权重。找出模式并从这些模式中提取知识是当前的主要任务。数据挖掘技术已被证明是这种知识发现的好方法。本研究利用了两种数据挖掘技术的突出特点,即k均值聚类和决策树。这两种方法,一种是无监督学习,另一种是监督学习,使用非常不同的方法来预测结果。利用这两种技术的优点建立了一个混合分类器。目的是为心脏病识别系统提供一种具有最佳准确性和性能的算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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