A hybrid method based on artificial immune system and fuzzy k-NN algorithm for diagnosis of heart valve diseases Abdulkadir Sengur * , Ibrahim Turkoglu Firat University, Department of Electronics and Computer Science, 23119 Elazig, Turkey Abstract 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. Ó 2007 Elsevier Ltd. All rights reserved. Keywords: Heart valve disease; Artificial immune system; Fuzzy k-NN classifier 1. Introduction According to the researches most of human deaths in the world are due to heart diseases. The heart valve disor- ders are one of the most important heart diseases. More- over, the most common disorder varieties in this field are aortic insufficiency, aortic stenosis, mitral insufficiency and mitral stenosis. For this reason, early detection of heart valve disorders is necessary in the medical research areas (Akay et al., 1992). Heart valve disease may be suspected if the heart sounds heard through a stethoscope are abnormal. This is usually the first step in diagnosing a heart valve disease. A charac- teristic heart murmur (abnormal sounds in the heart due to turbulent blood flow) can often indicate valve regurgita- tion. To further define the type of valve disease and extent of the valve damage, physicians may use any of the follow- ing diagnostic procedures; electrocardiogram (ECG or EKG), chest X-ray, cardiac catheterization, transesopha- geal echo (TEE), radionuclide scans and magnetic reso- nance imaging (MRI) (Nanda, 1993). However, each method is limited in its ability to offer efficient and thor- ough detection and characterization (Plett et al., 2000; Turkoglu, Arslan, & Ilkay, 2002). All of these methods are based on experience and information of physician. The researches in this area are focused on improving human–machine interfaces in existing methods. In this way, the cardiologist can understand the output of the examination systems more easily and diagnose the problem more accurately (Nanda, 1993; Turkoglu et al., 2002; Philpot, Yoganathan, & Nanda, 1993). In the last decade, Doppler technique has gained much more interest since Satomura first demonstrated the appli- cation of the Doppler Effect to the measurement of blood velocity in 1959 (Keeton & Schlindwein, 1997). Doppler heart sounds (DHS) are one of the most important sounds produced by blood flow, valves motion and vibration of the other cardiovascular components (Jing, Xuemin, Mingshi, & Wie, 1997). However, the factors such as calcified disease 0957-4174/$ - see front matter Ó 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.eswa.2007.08.003 * Corresponding author. E-mail address: ksengur@firat.edu.tr (A. Sengur). www.elsevier.com/locate/eswa Available online at www.sciencedirect.com Expert Systems with Applications 35 (2008) 1011–1020 Expert Systems with Applications