Identification of Heart Valve Disease using Bijective Soft Sets Theory
Identification of Heart Valve Disease using Bijective Soft Sets Theory
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DOI：
10.4018/ijrsda.2014070101
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发表时间：
2014-07
期刊：
影响因子：
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通讯作者：
S. U. Kumar;H. H. Inbarani-H.;A. Azar;A. Hassanien
中科院分区：
文献类型：
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作者：
S. U. Kumar;H. H. Inbarani-H.;A. Azar;A. Hassanien
心脏瓣膜疾病的主要并发症是充血性心脏瓣膜衰竭。心脏对人类具有重要意义。用听诊器听诊被认为是用于分析心脏病的技术之一。心脏听诊是一项困难的任务，以确定心脏状况，需要一些上级培训的医生。因此，在心音诊断中使用计算机化技术可以帮助医生在临床环境中。因此，在这项研究中，计算机辅助心音诊断进行，以支持医生的决策。在这项研究中，一种新的混合粗糙双射软集的心脏瓣膜疾病的分类。一个粗糙集（快速约简）为基础的特征选择技术应用前的分类，以提高分类精度。实验结果表明，所采用的改进双射软集方法（IBISOCLASS）提供的整体分类精度提供了更高的精度相比，其他分类技术，包括混合粗糙双射软集（RBISOCLASS），双射软集（BISOCLASS），决策表（DT），朴素贝叶斯（NB）和J48。
Major complication of heart valve diseases is congestive heart valve failure. The heart is of essential significance to human beings. Auscultation with a stethoscope is considered as one of the techniques used in the analysis of heart diseases. Heart auscultation is a difficult task to determine the heart condition and requires some superior training of medical doctors. Therefore, the use of computerized techniques in the diagnosis of heart sounds may help the doctors in a clinical environment. Hence, in this study computer-aided heart sound diagnosis is performed to give support to doctors in decision making. In this study, a novel hybrid Rough-Bijective soft set is developed for the classification of heart valve diseases. A rough set (Quick Reduct) based feature selection technique is applied before classification for increasing the classification accuracy. The experimental results demonstrate that the overall classification accuracy offered by the employed Improved Bijective soft set approach (IBISOCLASS) provides higher accuracy compared with other classification techniques including hybrid Rough-Bijective soft set (RBISOCLASS), Bijective soft set (BISOCLASS), Decision table (DT), Naive Bayes (NB) and J48.