心力衰竭患者的分类

T. Bayrak, H. Oğul
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引用次数: 1

摘要

超声心动图是利用超声换能器利用高频声波对心脏进行解剖和生理成像。用这种方法得到的信号被定义为超声心动图。通过这种方法,可以调查心脏的功能,并根据许多参数确定任何异常情况。本研究根据机器学习库(Machine Learning Repository, UCI)数据库中74例患者超声心动图信号中获得的7个特征实现了分类。朴素贝叶斯被确定为该数据集的最佳分类方法,灵敏度达到63%,特异性达到84%,准确率达到77%。总之,本研究提出了一项调查,确定哪些特征在心力衰竭死亡中具有重要意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Classification of patients with heart failure
Echocardiography is imaging of anatomy and physiology of heart with high frequency sound waves by using ultrasonic transducers. The signals obtained by using this method are defined as echocardiogram. In this way, the function of heart can be investigated and any abnormal case is determined according to many parameters. In this study, the classification was realized, according to 7 of features obtained from echocardiogram signals belong to 74 of patient in Machine Learning Repository (UCI) database. Naive Bayes was determined as the best classification method for this dataset and 63% sensitivity, 84% specificity, and an accuracy value of 77% has been reached. In conclusion, this study presents an investigation of determination of which features are significant in death based on heart failure.
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