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Artificial immune recognition system based classifier ensemble on the different feature subsets for detecting the cardiac disorders from SPECT images
(SPRINGER-VERLAG BERLIN, 2007) Polat, Kemal; Sekerci, Ramazan; Gunes, Salih
Combining outputs of multiple classifiers is one of most important techniques for improving classification accuracy. In this paper, we present a new classifier ensemble based on artificial immune recognition system (AIRS) classifier and independent component analysis (ICA) for detecting the cardiac disorders from SPECT images. Firstly, the dimension of SPECT (Single Photon Emission Computed Tomography) images dataset, which has 22 binary features, was reduced to 3, 4, and 5 features using FastICA algorithm. Three different feature subsets were obtained in this way. Secondly, the obtained feature subsets were classified by AIRS classifier and then stored the outputs obtained from AIRS classifier into the result matrix. The exact result that denote whether subject has cardiac disorder or not was obtained by averaging the outputs obtained from AIRS classifier into the result matrix. While only AIRS classifier obtained 84.96% classification accuracy with 50-50% train-test split for diagnosing the cardiac disorder from SPECT images, classifier ensemble based on AIRS and ICA fusion obtained 97.74% classification accuracy on the same conditions. The accuracy of AIRS classifier utilizing the reduced feature subsets was higher than those exploiting all the original features. These results show that the proposed ensemble method is very promising in diagnosis of the cardiac disorder from SPECT images.
Artificial immune recognition system with fuzzy resource allocation mechanism classifier, principal component analysis and FFT method based new hybrid automated identification system for classification of EEG signals
(PERGAMON-ELSEVIER SCIENCE LTD, 2008) Polat, Kemal; Guens, Salih
The aim of this study is to classification of EEG signals using a new hybrid automated identification system based on Artificial immune recognition system (AIRS) with fuzzy resource allocation mechanism, principal component analysis (PCA) and fast Fourier transform (FFT) method. EEG signals used belong to normal subject and patient that has epileptic seizure. The proposed system has three stages: (i) feature extraction using Welch (FFT) method, (ii) dimensionality reduction using PCA, and (iii) EEG classification using AIRS with fuzzy resource allocation. We have used the 10-fold cross-validation, classification accuracy, sensitivity and specificity analysis, and confusion matrix to show the robustness and efficient of proposed system. The obtained classification accuracy is about 100% and it is very promising compared to the previously reported classification techniques. (c) 2007 Elsevier Ltd. All rights reserved.
Automated Identification of Diseases Related to Lymph System From Lymphography Data Using Artificial Immune Recognition System With Fuzzy Resource Allocation Mechanism (Fuzzy-Airs)
(Elsevier Sci Ltd, 2006) Polat, Kemal; Güneş, Salih
Artificial immune recognition system (AIRS) classification algorithm, which has an important place among classification algorithms in the field of artificial immune systems, has showed an effective and intriguing performance oil the problems it was applied. AIRS was previously applied to some medical classification problems including breast cancer, Cleveland heart disease, diabetes and it obtained very satisfactory results. So, AIRS proved to be,in efficient artificial intelligence technique in medical field. In this study, the resource allocation mechanism of AIRS was changed with a new one determined by fuzzy-logic. This system, named as fuzzy-AIRS was used as a classifier in the diagnosis of lymph diseases, which is of great importance in medicine. The classifications of lymph diseases dataset taken from University of California at Irvine (UCI) Machine Learning Repository were done using 10-fold cross-validation method. Reached classification accuracies were evaluated by comparing them with reported classifiers in UCI web site in addition to other systems that are applied to the related problems. Also, the obtained classification performances were compared with AIRS with regard to the classification accuracy, number of resources and classification time. While only AIRS algorithm obtained 83.138% classification accuracy, fuzzy-AIRS classified the lymph diseases dataset with 90.00% accuracy. For lymph diseases dataset, fuzzy-AIRS obtained the highest classification accuracy according to the UCI web site, Beside of this success, fuzzy-AIRS gained an important advantage over the AIRS by means of classification time. By reducing classification time as well as obtaining high classification accuracies in the applied datasets, fuzzy-AIRS classifier proved that it could be used as an effective classifier for medical problems.
Automatic detection of heart disease using an artificial immune recognition system (AIRS) with fuzzy resource allocation mechanism and k-nn (nearest neighbour) based weighting preprocessing
(PERGAMON-ELSEVIER SCIENCE LTD, 2007) Polat, Kemal; Sahan, Seral; Guenes, Salih
It is evident that usage of machine learning methods in disease diagnosis has been increasing gradually. In this study, diagnosis of heart disease, which is a very common and important disease, was conducted with such a machine learning system. In this system, a new weighting scheme based on k-nearest neighbour (k-nn) method was utilized as a preprocessing step before the main classifier. Artificial immune recognition system (AIRS) with fuzzy resource allocation mechanism was our used classifier. We took the dataset used in our study from the UCI Machine Learning Database. The obtained classification accuracy of our system was 87% and it was very promising with regard to the other classification applications in the literature for this problem. (C) 2006 Elsevier Ltd. All rights reserved.
Automatic determination of diseases related to lymph system from lymphography data using principles component analysis (PCA), fuzzy weighting pre-processing and ANFIS
(PERGAMON-ELSEVIER SCIENCE LTD, 2007) Polat, Kemal; Gunes, Salih
It is evident that usage of machine learning methods in disease diagnosis has been increasing gradually. In this study, diagnosis of lymph diseases, which is a very common and important disease, was conducted with such a machine learning system. In this study, we have detected on lymph diseases using principles component analysis (PCA), fuzzy weighting pre-processing and adaptive neuro-fuzzy inference system (ANFIS). The approach system has three stages. In the first stage, dimension of lymph diseases dataset that has 18 features is reduced to four features using principles component analysis. In the second stage, a new weighting scheme based on fuzzy weighting method was utilized as a pre-processing step before the main classifier. Then, in the third stage, ANFIS was our used classifier. We took the lymph diseases dataset used in our study from the UCI machine learning database. The obtained classification accuracy of our system was 88.83% and it was very promising with regard to the other classification applications in the literature for this problem. (c) 2006 Elsevier Ltd. All rights reserved.
Automatic determination of traffic accidents based on KMC-based attribute weighting
(SPRINGER, 2012) Polat, Kemal; Durduran, S. Savas
In this study, the traffic accidents recognizing risk factors related to the environmental (climatological) conditions that are associated with motor vehicles accidents on the Konya-Afyonkarahisar highway with the aid of Geographical Information Systems (GIS) have been determined using the combination of K-means clustering (KMC)-based attribute weighting (KMCAW) and classifier algorithms including artificial neural network (ANN) and adaptive network-based fuzzy inference system (ANFIS). The dynamic segmentation process in ArcGIS9.0 from the traffic accident reports recorded by District Traffic Agency has identified the locations of the motor vehicle accidents. The attributes obtained from this system are day, temperature, humidity, weather conditions, and month of occurred traffic accidents. The traffic accident dataset comprises five attributes (day, temperature, humidity, weather conditions, and month of occurred traffic accidents) and 358 observations including 179 without accident and 179 with accident. The proposed comprises two stages. In the first stage, the all attributes of dataset have been weighted using KMCAW method. The aims of this weighting method are both to increase the classification performance of used classifier algorithm and to transform from linearly non-separable traffic accidents dataset to a linearly separable dataset. In the second stage, after weighting process, ANN and ANFIS classifier algorithms have been separately used to determine the case of traffic accidents as with accident or without accident. In order to evaluate the performance of proposed method, the classification accuracy, sensitivity, specificity and area under the ROC (Receiver Operating Characteristic) curves (AUC) values have been used. While ANN and ANFIS classifiers obtained the overall prediction accuracies of 53.93 and 38.76%, respectively, the combination of KMCAW and ANN and the combination of KMCAW and ANFIS achieved the overall prediction accuracies of 74.15 and 55.06% on the prediction of traffic accidents. The experimental results have demonstrated that the proposed attribute weighting method called KMCAW is a robust and effective data pre-processing method in the prediction of traffic accidents on Konya-Afyonkarahisar highway in Turkey.
Biyomedikal sinyallerde veri ön-işleme tekniklerinin medikal teşhiste sınıflama doğruluğuna etkisinin incelenmesi
(Selçuk Üniversitesi Fen Bilimleri Enstitüsü, 2008) Polat, Kemal; Güneş, Salih
Bu tez çalışmasında, biyomedikal veri kümelerinin sınıflandırılmasında sınıflama performansını arttırmak için veri ağırlıklandırma ve özellik seçme yöntemleri önerilmiş ve kullanılmıştır. Biyomedikal veri kümelerini sınıflamada sınıflama performansını azaltan bazı etmenler vardır. Bu etmenler gürültü, aykırı değer, lineer olmayan bir veri dağılımına sahip olma gibi durumlardır. Yukarıdaki etmenlere sahip olan veri kümelerinin sınıflama performanslarını arttırmak için çeşitli veri ön-işleme teknikleri kullanılır. Biyomedikal veri kümelerinde, özellik çıkarımından sonra oluşturulan veri setinin boyutu fazla olabilir veya veri setinde ilgisiz/fazla özellikler olabilir. Bu özelliklerin dezavantajları; sınıflama performansını azaltır ve sınıflandırıcının hesaplama maliyetini arttırır. Yapılan çalışmalarda, özellik seçme algoritmaları ile daha yüksek genelleştirme yeteneği ve daha az işlem karışıklığı elde edilmiştir. Bu tez çalışmasında, boyut azaltımı ve özellik seçme algoritması olarak, temel bileşen analizi, bilgi kazancına dayanan özellik seçme algoritması ve Kernel F-skor özellik seçme yöntemleri özelik seçme algoritmaları olarak kullanılmıştır. Bu yöntemler arasında, özellik seçme olarak, bilgi kazancına dayanan özellik seçme algoritması ile Kernel F-skor özellik seçme yöntemi ön plana çıkmaktadır. Boyut azaltımı olarak da temel bileşen analizine ağırlık verilmiştir. Veri ağırlıklandırma yöntemleri olarak, bulanık ağırlıklandırma ön-işleme, k-NN (k-en yakın komşu) tabanlı veri ağırlıklandırma ön-işleme, genelleştirilmiş ayrışım analizi ve benzerlik tabanlı veri ağırlıklandırma ön-işleme yöntemleri medikal veri kümelerini sınıflamada sınıflama performansını iyileştirmek için kullanılmış ve önerilmiştir. Bu tez çalışmasında kullanılan biyomedikal veri kümeleri; kalp hastalığı, SPECT (Single Photon Emission Computed Tomography) görüntüleri ile kalp hastalığı, E.coli Promoter gen dizileri, Doppler sinyali ile damar sertliği (Atherosclerosis) hastalığı, VEP (Görsel Uyarılmış Potansiyel) sinyali ile optik sinir hastalığı ve PERG (Örüntü Retinografisi) sinyali ile Macular hastalığı veri kümeleridir. Bu veri kümeleri içinden, kalp hastalığı, SPECT (Single Photon Emission Computed Tomography) görüntüleri ile kalp hastalığı, E.coli Promoter gen dizileri veri kümeleri, UCI (University of California, Irvine) makine öğrenmesi veritabanından alınmıştır. Doppler sinyali ile damar sertliği hastalığı, VEP sinyali ile optik sinir hastalığı ve PERG sinyali ile Macular hastalığı veri kümeleri ise Fatih Üniversitesi Öğretim Üyesi Prof. Dr. Sadık Kara ve Erciyes Üniversitesi Biyomedikal Mühendisliği ekibi tarafından alınan verilerdir. Veri ön-işleme ve özellik seçme yöntemlerinin performanslarını değerlendirmek için bu yöntemler sınıflama algoritmaları ile hibrid olarak kullanılmışlardır. Kullanılan sınıflama algoritmaları, ANFIS (Adaptif Ağ Tabanlı Bulanık Çıkarım Sistemi), C4.5 karar ağacı, YBTS (Yapay Bağışıklık Tanıma Sistemi), bulanık kaynak dağılım mekanizmalı YBTS ve yapay sinir ağlarıdır. Biyomedikal veri kümelerinin sınıflandırılması sonucunda, veri ağırlıklandırma yöntemleri arasında en iyi sonuçları veren yöntem, k-NN (k- en yakın komşu) tabanlı veri ağırlıklandırma yöntemi olmuştur. Özellik seçme yöntemleri arasında ise temel bileşen analizi diğer özellik seçme yöntemlere göre üstün sonuçlar elde etmiştir. Özellik seçme yöntemleri, veri ağırlıklandırma yöntemleri ile sınıflama algoritmaları birleştirilerek 12 yeni hibrid sistem oluşturulmuş ve bu yeni hibrid sistemler tezde kullanılan 6 medikal veri kümesine uygulanmıştır. Hesaplama maliyeti ve sınıflama performansı açısından her bir medikal veri kümesi için en iyi hibrid model seçilmiştir.
Breast cancer and liver disorders classification using artificial immune recognition system (AIRS) with performance evaluation by fuzzy resource allocation mechanism
(PERGAMON-ELSEVIER SCIENCE LTD, 2007) Polat, Kemal; Sahan, Seral; Kodaz, Halife; Guenes, Salih
Artificial Immune Recognition System (AIRS) classification algorithm, which has an important place among classification algorithms in the field of Artificial Immune Systems, has showed an effective and intriguing performance on the problems it was applied. AIRS was previously applied to some medical classification problems including Breast Cancer, Cleveland Heart Disease, Diabetes and it obtained very satisfactory results. So, AIRS proved to be an efficient artificial intelligence technique in medical field. In this study, the resource allocation mechanism of AIRS was changed with a new one determined by Fuzzy-Logic. This system, named as Fuzzy-AIRS was used as a classifier in the diagnosis of Breast Cancer and Liver Disorders, which are of great importance in medicine. The classifications of Breast Cancer and BUPA Liver Disorders datasets taken from University of California at Irvine (UCI) Machine Learning Repository were done using 10-fold cross-validation method. Reached classification accuracies were evaluated by comparing them with reported classifiers in UCI web site in addition to other systems that are applied to the related problems. Also, the obtained classification performances were compared with AIRS with regard to the classification accuracy, number of resources and classification time. Fuzzy-AIRS, which reached to classification accuracy of 98.51% for breast cancer, classified the Liver Disorders dataset with 83.36% accuracy. For both datasets, Fuzzy-AIRS obtained the highest classification accuracy according to the UCI web site. Beside of this success, Fuzzy-AIRS gained an important advantage over the AIRS by means of classification time. In the experiments, it was seen that the classification time in Fuzzy-AIRS was reduced about 70% of AIRS for both datasets. By reducing classification time as well as obtaining high classification accuracies in the applied datasets, Fuzzy-AIRS classifier proved that it could be used as an effective classifier for medical problems. (C) 2005 Elsevier Ltd. All rights reserved.
Breast cancer diagnosis using least square support vector machine
(ACADEMIC PRESS INC ELSEVIER SCIENCE, 2007) Polat, Kemal; Guenes, Salih
The use of machine learning tools in medical diagnosis is increasing gradually. This is mainly because the effectiveness of classification and recognition systems has improved in a great deal to help medical experts in diagnosing diseases. Such a disease is breast cancer, which is a very common type of cancer among woman. In this paper, breast cancer diagnosis was conducted using least square support vector machine (LS-SVM) classifier algorithm. The robustness of the LS-SVM is examined using classification accuracy, analysis of sensitivity and specificity, k-fold cross-validation method and confusion matrix. The obtained classification accuracy is 98.53% and it is very promising compared to the previously reported classification techniques. Consequently, by LS-SVM, the obtained results show that the used method can make an effective interpretation and point out the ability of design of a new intelligent assistance diagnosis system. (c) 2006 Elsevier Inc. All rights reserved.
A cascade learning system for classification of diabetes disease: Generalized discriminant analysis and least square support vector machine
(PERGAMON-ELSEVIER SCIENCE LTD, 2008) Polat, Kemal; Guenes, Salih; Arslan, Ahmet
The aim of this study is to diagnosis of diabetes disease, which is one of the most important diseases in medical field using Generalized Discriminant Analysis (GDA) and Least Square Support Vector Machine (LS-SVM). Also, we proposed a new cascade learning system based on Generalized Discriminant Analysis and Least Square Support Vector Machine. The proposed system consists of two stages. The first stage, we have used Generalized Discriminant Analysis to discriminant feature variables between healthy and patient (diabetes) data as pre-processing process. The second stage, we have used LS-SVM in order to classification of diabetes dataset. While LS-SVM obtained 78.21% classification accuracy using 10-fold cross validation, the proposed system called GDA-LS-SVM obtained 82.05% classification accuracy using 10-fold cross validation. The robustness of the proposed system is examined using classification accuracy, k-fold cross-validation method and confusion matrix. The obtained classification accuracy is 82.05% and it is very promising compared to the previously reported classification techniques. (c) 2006 Elsevier Ltd. All rights reserved.
Classification of epileptiform EEG using a hybrid system based on decision tree classifier and fast Fourier transform
(ELSEVIER SCIENCE INC, 2007) Polat, Kemal; Guenes, Salih
The aim of this study is to detect epileptic seizure in EEG signals using a hybrid system based on decision tree classifier and fast Fourier transform (FFT). The present study proposes a hybrid system with two stages: feature extraction using FFT and decision making using decision tree classifier. The detection of epileptiform, discharges in the electroencephalogram (EEG) is an important part in the diagnosis of epilepsy. All data set were obtained from EEG signals of healthy subjects and subjects suffering from epilepsy diseases. For healthy subjects is background EEG (scalp) with open eyes and for epileptic patients correspond to a seizure recorded in hippocampus (epileptic focus) with depth electrodes. The evolution of proposed system was conducted using k-fold cross-validation, classification accuracy, and sensitivity and specificity values. We have obtained 98.68% and 98.72% classification accuracies using 5- and 10-fold cross-validation. The stated results show that the proposed method could point out the ability of design of a new intelligent assistance diagnosis system. (C) 2006 Elsevier Inc. All rights reserved.
Classification of vertebral column disorders and lumbar discs disease using attribute weighting algorithm with mean shift clustering
(ELSEVIER SCI LTD, 2016) Unal, Yavuz; Polat, Kemal; Kocer, H. Erdinc
In this article, a new data pre-processing method has been suggested to detect and classify vertebral column disorders and lumbar disc diseases with a high accuracy level. The suggested pre-processing method is called the Mean Shift Clustering-Based Attribute Weighting (MSCBAW) and is based primarily on mean shift clustering algorithm finding the number of the sets automatically. In this study, we have used two different datasets including lumbar disc diseases (with two classes-our database) and vertebral column disorders datasets (with two or three classes) taken from UCI (University of California at Irvine) machine learning database to test the proposed approach. The MSCBAW method is working as follows: first of all, the centres of the sets automatically for each characteristics in dataset by using the mean shift clustering algorithm are computed. And then, the mean values of each property in dataset are calculated. The weighted datasets by multiplying these mean values by each property value in the dataset that have been obtained by dividing the above mentioned mean values by the centres of the sets belonging to the relevant property are achieved. After the data weighting stage, three different classification algorithms that included the k-NN (k-Nearest Neighbour), RBF-NN (Radial Basis Function-Neural Network) and SVM (Support Vector Machine) classifying algorithms have been used to classify the datasets. In the classification of vertebral column disorders dataset with two classes (normal or abnormal), while the obtained classification accuracies and kappa values were 78.70% +/- 0.455 (the classification accuracy +/- standard deviation), 81.93% +/- 0.899, and 80.32% +/- 0.56 using SVM, k-NN (for k = 1), and RBF-NN classifiers, respectively, the combinations of MSCBAW and SVM, k-NN (for k = 1), and RBF-NN classifiers were obtained 99.03% +/- 0.977, 99.67% +/- 0.992, and 99.35% +/- 0.9852, respectively. In the classification of second dataset named vertebral column disorders dataset with three classes (Normal, Disk Hernia, and Spondylolisthesis), while the obtained classification accuracies and kappa values were 74.51% +/- 0.581, 78.70% +/- 0.659, and 83.22% +/- 0.728 using SVM, k-NN (for k = 1), and RBF-NN classifiers, respectively, the combinations of MSCBAW and SVM, k-NN (for k = 1), and RBF-NN classifiers were obtained 99.35% +/- 0.989, 96.77% +/- 0.948, and 99.67% +/- 0.994, respectively. As for the lumbar disc dataset, while the obtained classification accuracies and kappa values were 94.54% +/- 0.974, 94.54% +/- 0.877, and 93.45% +/- 0.856 using SVM, k-NN (for k = 1), and RBF-NN classifiers, respectively, the combinations of MSCBAW and SVM, k-NN (for k = 1), and RBF-NN classifiers were obtained 100% +/- 1.00, 99.63% +/- 0.991, and 99.63% +/- 0.991, respectively. The best hybrid models in the classification of vertebral column disorders dataset with two classes, vertebral column disorders dataset with three classes, and lumbar disc dataset were the combination of MSCBAW and k-NN classifier, the combination of MSCBAW and RBF-NN classifier, and the combination of MSCBAW and SVM classifier, respectively. (C) 2015 Elsevier Ltd. All rights reserved.
Comparison of different classifier algorithms for diagnosing macular and optic nerve diseases
(WILEY, 2009) Polat, Kemal; Kara, Sadik; Guven, Aysegul; Gunes, Salih
The aim of this research was to compare classifier algorithms including the C4.5 decision tree classifier, the least squares support vector machine (LS-SVM) and the artificial immune recognition system (AIRS) for diagnosing macular and optic nerve diseases from pattern electroretinography signals. The pattern electroretinography signals were obtained by electrophysiological testing devices from 106 subjects who were optic nerve and macular disease subjects. In order to show the test performance of the classifier algorithms, the classification accuracy, receiver operating characteristic curves, sensitivity and specificity values, confusion matrix and 10-fold cross-validation have been used. The classification results obtained are 85.9%, 100% and 81.82% for the C4.5 decision tree classifier, the LS-SVM classifier and the AIRS classifier respectively using 10-fold cross-validation. It is shown that the LS-SVM classifier is a robust and effective classifier system for the determination of macular and optic nerve diseases.
Comparison of different classifier algorithms on the automated detection of obstructive sleep apnea syndrome
(SPRINGER, 2008) Polat, Kemal; Yosunkaya, Sebnem; Gunes, Salih
In this paper, we have compared the classifier algorithms including C4.5 decision tree, le artificial neural network (ANN), artificial immune recognition system (AIRS), and adaptive neuro-fuzzy inference system (ANFIS) in the diagnosis of obstructive sleep apnea syndrome (OSAS), which is an important disease that affects both the right and the left cardiac ventricle. The goal of this study was to find the best classifier model on the diagnosis of OSAS. The clinical features were obtained from Polysomnography device as a diagnostic tool for obstructive sleep apnea in patients clinically suspected of suffering this disease in this study. The clinical features are arousals index, apnea-hypopnea index (AHI), SaO(2) minimum value in stage of rapid eye movement, and percent sleep time in stage of SaO(2) intervals bigger than 89%. In our experiments, a total of 83 patients (58 with a positive OSAS (AHI > 5) and 25 with a negative OSAS such that normal subjects) were examined. The decision support systems can help to physicians in the diagnosing of any disorder or disease using clues obtained from signal or images taken from subject having any disorder. In order to compare the used classifier algorithms, the mean square error, classification accuracy, area under the receiver operating characteristics curve (AUC), and sensitivity and specificity analysis have been used. The obtained AUC values of C4.5 decision tree, ANN, AIRS, and ANFIS classifiers are 0.971, 0.96, 0.96, and 0.922, respectively. These results have shown that the best classifier system is C4.5 decision tree classifier on the diagnosis of obstructive sleep apnea syndrome.
Computer aided diagnosis of ECG data on the least square support vector machine
(ACADEMIC PRESS INC ELSEVIER SCIENCE, 2008) Polat, Kemal; Akdemir, Bayram; Gunes, Salih
In this paper we describe a technique that has successfully classified arrhythmia from an ECG dataset using a least square support vector machine (LSSVM). LSSVM was applied to the ECG dataset to distinguish between healthy persons and diseased persons (arrhythmia). The LSSVM classifier trained with four train-test parts including a training-to-test split of 50-50%, a training-to-test split of 70-30%, and a training-to-test split of 80-20%. We have used the classification accuracy, sensitivity and specificity analysis, and ROC curves to test the performance of LSSVM classifier on the detection of ECG arrhythmia. The classification accuracies obtained are 100% for all the training-to-test splits. These results show that the proposed method is more promising than previously reported classification techniques. The results suggest that the proposed method can be used to enhance the performance of a new intelligent assistance diagnosis system. (C) 2007 Elsevier Inc. All rights reserved.
Computer aided medical diagnosis system based on principal component analysis and artificial immune recognition system classifier algorithm
(PERGAMON-ELSEVIER SCIENCE LTD, 2008) Polat, Kemal; Guenes, Salih
In this study, diagnosis of lung cancer, which is a very common and important disease, was conducted with computer aided medical diagnosis system based on principal component analysis and artificial immune recognition system. The approach system has two stages. In the first stage, dimension of lung cancer dataset that has 57 features is reduced to 4 features using principal component analysis. In the second stage, artificial immune recognition system (AIRS) was our used classifier. We took the lung cancer dataset used in our study from the UCI (from University of California, Department of Information and Computer Science) Machine Learning Database. The obtained classification accuracy of our system was 100% and it was very promising with regard to the other classification applications in literature for this problem. (c) 2006 Elsevier Ltd. All rights reserved.
Detection of abnormalities in lumbar discs from clinical lumbar MRI with hybrid models
(ELSEVIER, 2015) Unal, Yavuz; Polat, Kemal; Kocer, H. Erdinc; Hariharan, M.
Disc abnormalities cause a great number of complaints including lower back pain. Lower back pain is one of the most common types of pain in the world. The computer-assisted detection of this ailment will be of great use to physicians and specialists. With this study, hybrid models have been developed which include feature extraction, selection and classification characteristics for the purpose of determining the disc abnormalities in the lumbar region. In determining the abnormalities, T2-weighted sagittal and axial Magnetic Resonance Images (MRI) were taken from 55 people. In the feature extraction stage, 27 appearance characteristics and form characteristics were acquired from both sagittal and transverse images. In the feature selection stage, the F-Score-Based Feature Selection (FSFS) and the Correlation-Based Feature Selection (CBFS) methods were used to select the best discriminative features. The number of features was reduced to 5 from 27 by using the FSFS, and to 22 from 27 by using the CBFS. In the last stage, five different classification algorithms, i.e. the Multi-Layer Perceptron, the Support Vector Machine, the Decision Tree, the Naive Bayes, and the k Nearest Neighbor algorithms were applied. In addition, the combination of the classifier model (the combination of the bagging and the random forests) has been used to improve the classification performance in the detection of lumbar disc datasets. The results which were obtained suggest that the proposed hybrid models can be used safely in detecting the disc abnormalities. (C) 2015 Elsevier B.V. All rights reserved.
Diagnosis of Heart Disease Using Artificial Immune Recognition System and Fuzzy Weighted Pre-Processing
(Pergamon-Elsevier Science Ltd, 2006) Polat, Kemal; Güneş, Salih; Tosun, Sülayman
This paper presents a novel method for diagnosis of heart disease. The proposed method is based on a hybrid method that uses fuzzy weighted pre-processing and artificial immune recognition system (AIRS). Artificial immune recognition system has showed an effective performance on several problems such as machine learning benchmark problems and medical classification problems like breast cancer, diabetes, liver disorders classification. The robustness of the proposed method is examined using classification accuracy, k-fold cross-validation method and confusion matrix. The obtained classification accuracy is 96.30% and it is very promising compared to the previously reported classification techniques.
Diagnosis of heart disease using artificial immune recognition system and fuzzy weighted pre-processing (vol 39, pg 2186, 2006)
(ELSEVIER SCI LTD, 2011) Polat, Kemal; Gunes, Salih; Tosun, Sulayman
[Abstract not Available]
EEG, EOG ve Çene EMG Sinyallerinin Zaman Domeni Özelliklerinin Uyku Evreleri ile İlişkisinin İncelenmesi
(2009) Güneş, Salih; Polat, Kemal; Dursun, Mehmet; Yosunkaya, Şebnem
Sleep staging has an important role in determining sleep disorders such as sleepiness, human fatigue etc. Sleep staging is generally done according to Rechtschaffen and Kales standard (RKS) using EEG signal obtained from PSG signals taken from patient subjects who come with any sleep disorders. Sleep stages are generally divided into three stages including awake, REM and N-REM (stage 1, stage 2, and stage 3). In this study, time domain features of EEG, EOG of right and left eyes, and chin EMG signals belonging to sleep stages were investigated and correlation between these time domain features and sleep stages was calculated. The used time domain features are mean value, standard deviation, peak value, skewness, kurtosis, and shape factor belonging to EEG, EOG of right and left eyes, and chin EMG signals. In experimental studies, PSG recordings of 3 subjects were taken and average recording time of 6.22 h, total recording time was 18.67 h. When investigated correlation coefficients, it is seen that skewness feature in time domain features of EEG signal, standard deviation feature in time domain features of EOG signals belonging to right and left eyes, and mean value feature in time domain features of chin EMG signal were more correlated with sleep stages than other features. Consequently, a feature vector can be constituted combining features determined from time domain features of EEG, EOG belonging to right and left eyes, and chin EMG signals. This obtained feature vector can be easily used in distinguishing sleep stages.

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