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A hybrid method based on artificial immune system and fuzzy k-NN algorithm for diagnosis of heart valve diseases

机译:基于人工免疫系统和模糊k-NN算法的混合方法在心脏瓣膜疾病诊断中的应用

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摘要

The use of artificial intelligence methods in medical analysis is increasing. This is mainly because the effectiveness of classification and detection systems has improved in a great deal to help medical experts in diagnosing. In this paper, we investigate the performance of an artificial immune system (AIS) based fuzzy k-NN algorithm to determine the heart valve disorders from the Doppler heart sounds. The proposed methodology is composed of three stages. The first stage is the pre-processing stage. The feature extraction is the second stage. During feature extraction stage, Wavelet transforms and short time Fourier transform were used. As next step, wavelet entropy was applied to these features. In the classification stage, AIS based fuzzy k-NN algorithm is used. To compute the correct classification rate of proposed methodology, a comparative study is realized by using a data set containing 215 samples. The validation of the proposed method is measured by using the sensitivity and specificity parameters. 95.9% sensitivity and 96% specificity rate was obtained.
机译:人工智能方法在医学分析中的使用正在增加。这主要是因为分类和检测系统的有效性已大大提高,可帮助医学专家进行诊断。在本文中,我们研究了基于人工免疫系统(AIS)的模糊k-NN算法从多普勒心音中确定心脏瓣膜疾病的性能。所提出的方法包括三个阶段。第一阶段是预处理阶段。特征提取是第二阶段。在特征提取阶段,使用了小波变换和短时傅立叶变换。下一步,将小波熵应用于这些特征。在分类阶段,使用基于AIS的模糊k-NN算法。为了计算所提出方法的正确分类率,使用包含215个样本的数据集进行了比较研究。通过使用敏感性和特异性参数来测量所提出方法的有效性。获得了95.9%的灵敏度和96%的特异性率。

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