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ABSTRACT: Artificial Immune Recognition System (AIRS) classifier algorithm is robust and effective in medical dataset classification
applications such as breast cancer, heart disease, diabetes diagnosis etc. In our previous work, we have proposed a new resource
allocation mechanism called fuzzy resource allocation in AIRS algorithm both to improve the classification accuracy and to
decrease the computation time in classification process. Here, AIRS and Fuzzy-AIRS classifier algorithms and one against all
approach have been combined to increase the classification accuracy of obstructive sleep apnea syndrome (OSAS) that is an
important disease that influences both the right and the left cardiac ventricle. The OSAS dataset consists of four classes
including of normal (25 subjects), mild OSAS (AHI (Apnea and Hypoapnea Index) =5-15 and 14 subjects), moderate OSAS (AHI < 15-30
and 18 subjects), and serious OSAS (AHI > 30 and 26 subjects). In the extracting of features that is characterized the OSAS
disease, the clinical features obtained from Polysomnography used diagnostic tool for obstructive sleep apnea in patients
clinically suspected of suffering from this disease have been used. The used clinical features are Arousals Index (ARI), Apnea
and Hypoapnea Index (AHI), SaO2 minimum value in stage of REM, and Percent Sleep Time (PST) in stage of SaO2 intervals bigger
than 89%. Even though AIRS and Fuzzy-AIRS classifiers have been used in the classifying multi-class problems, theirs classification
performances are low in the case of multi-class classification problems. Therefore, we have used two classes in AIRS and Fuzzy-AIRS
classifiers by means of one against all approach instead of four classes comprising the healthy subjects, mild OSAS, moderate
OSAS, and serious OSAS. We have applied the AIRS, Fuzzy-AIRS, AIRS with one against all approach (Pairwise AIRS), and Fuzzy-AIRS
with one against all approach (Pairwise Fuzzy-AIRS) to OSAS dataset. The obtained classification accuracies are 63.41%, 63.41%,
87.19%, and 84.14% using the above methods for 200 resources, respectively. These results show that the best method for diagnosis
of OSAS is the combination of AIRS and one against all approach (Pairwise AIRS).
Journal of Medical Systems 04/2012; 32(6):489-497. · 1.13 Impact Factor
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ABSTRACT: Rheumatoid arthritis (RA) is a multi-systemic autoimmune disease that leads to substantial morbidity and mortality. In this paper, as spectral analysis methods of Multiple Signal Classification (MUSIC) method is used in order to extract the significant features from the right and left hand Ulnar artery Doppler signals for the diagnosis of RA disease. The MUSIC method has been used as subspace method. To extract features from Doppler signals obtained from the right and left hand Ulnar arterial the MUSIC method model degrees of 5, 10, 15, 20, and 25 were used. Then, an adaptive network based fuzzy inference system (ANFIS) was applied to features extracted from the right and left hand Ulnar artery Doppler signals for classifying RA disease. The methods are not new, but the study has a novelty in that the application area of these methods is new. In the hybrid model, the combination of MUSIC and ANFIS yielded classification accuracies of 95% (for a model degree of 20) using the right hand Ulnar artery and classification accuracies of 91.25% (for a model degree of 10) using left hand Ulnar artery Doppler signals in the diagnosis of RA disease. The proposed approach has potential to help with the early diagnosis of RA disease for the specialists who study this subject.
Advances in Engineering Software. 01/2010;
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ABSTRACT: 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.
Expert Systems 01/2009; 26(1):22 - 34. · 0.68 Impact Factor
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[hide abstract]
ABSTRACT: Artificial Immune Recognition System (AIRS) classifier algorithm is robust and effective in medical dataset classification applications such as breast cancer, heart disease, diabetes diagnosis etc. In our previous work, we have proposed a new resource allocation mechanism called fuzzy resource allocation in AIRS algorithm both to improve the classification accuracy and to decrease the computation time in classification process. Here, AIRS and Fuzzy-AIRS classifier algorithms and one against all approach have been combined to increase the classification accuracy of obstructive sleep apnea syndrome (OSAS) that is an important disease that influences both the right and the left cardiac ventricle. The OSAS dataset consists of four classes including of normal (25 subjects), mild OSAS (AHI (Apnea and Hypoapnea Index) = 5-15 and 14 subjects), moderate OSAS (AHI < 15-30 and 18 subjects), and serious OSAS (AHI > 30 and 26 subjects). In the extracting of features that is characterized the OSAS disease, the clinical features obtained from Polysomnography used diagnostic tool for obstructive sleep apnea in patients clinically suspected of suffering from this disease have been used. The used clinical features are Arousals Index (ARI), Apnea and Hypoapnea Index (AHI), SaO2 minimum value in stage of REM, and Percent Sleep Time (PST) in stage of SaO2 intervals bigger than 89%. Even though AIRS and Fuzzy-AIRS classifiers have been used in the classifying multi-class problems, theirs classification performances are low in the case of multi-class classification problems. Therefore, we have used two classes in AIRS and Fuzzy-AIRS classifiers by means of one against all approach instead of four classes comprising the healthy subjects, mild OSAS, moderate OSAS, and serious OSAS. We have applied the AIRS, Fuzzy-AIRS, AIRS with one against all approach (Pairwise AIRS), and Fuzzy-AIRS with one against all approach (Pairwise Fuzzy-AIRS) to OSAS dataset. The obtained classification accuracies are 63.41%, 63.41%, 87.19%, and 84.14% using the above methods for 200 resources, respectively. These results show that the best method for diagnosis of OSAS is the combination of AIRS and one against all approach (Pairwise AIRS).
Journal of Medical Systems 01/2009; 32(6):489-97. · 1.13 Impact Factor
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ABSTRACT: The optic nerve disease is an important disease that appears commonly in public. In this paper, we propose a hybrid diagnostic system based on discretization (quantization) method and classification algorithms including C4.5 decision tree classifier, artificial neural network (ANN), and least square support vector machine (LSSVM) to diagnose the optic nerve disease from Visual Evoked Potential (VEP) signals with discrete values. The aim of this paper is to investigate the effect of Discretization method on the classification of optic nerve disease. Since the VEP signals are non-linearly-separable, low classification accuracy can be obtained by classifier algorithms. In order to overcome this problem, we have used the Discretization method as data pre-processing. The proposed method consists of two phases: (i) quantization of VEP signals using Discretization method, and (ii) diagnosis of discretized VEP signals using classification algorithms including C4.5 decision tree classifier, ANN, and LSSVM. The classification accuracies obtained by these hybrid methods (combination of C4.5 decision tree classifier-quantization method, combination of ANN-quantization method, and combination of LSSVM-quantization method) with and without quantization strategy are 84.6-96.92%, 94.20-96.76%, and 73.44-100%, respectively. As can be seen from these results, the best model used to classify the optic nerve disease from VEP signals is obtained for the combination of LSSVM classifier and quantization strategy. The obtained results denote that the proposed method can make an effective interpretation and point out the ability of design of a new intelligent assistance diagnosis system.
Computer Methods and Programs in Biomedicine 10/2008; 91(3):255-64. · 1.52 Impact Factor
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ABSTRACT: Obstructive sleep apnea syndrome (OSAS) is an important disease that affects both the right and the left cardiac ventricle. This paper presents a novel classification method called pairwise ANFIS based on Adaptive Neuro-Fuzzy Inference System (ANFIS) and one against all method for detecting the obstructive sleep apnea syndrome. In order to extract the features related with OSAS, we have used the clinical features obtained from Polysomnography device as a diagnostic tool for obstructive sleep apnea (OSA) in patients clinically suspected of suffering from this disease. The clinical features obtained from Polysomnography Reports are Arousals Index (ARI), Apnea and Hypoapnea Index (AHI), SaO2 minimum value in stage of REM, and Percent Sleep Time (PST) in stage of SaO2 intervals bigger than 89%. Since ANFIS has output with one class, we have extended the output of ANFIS to multi class by means of one against all method to diagnose the OSAS that has four classes consisting of normal (25 subjects), mild OSAS (AHI=5-15 and 14 subjects), middle OSAS (AHI<15-30 and 18 subjects), and heavy OSAS (AHI>30 and 26 subjects). The classification accuracy, sensitivity and specifity analysis, mean square error, and confusion matrix have been used to test the performance of proposed method. The obtained classification accuracies are 82.92%, 82.92%, 85.36%, and 87.80% for each class including normal, mild OSAS, middle OSAS, and heavy OSAS using ANFIS with one against all method with 50-50% train-test split, respectively. Combining ANFIS and one against all method that is firstly proposed by us was firstly applied for diagnosing the OSAS. The proposed method has produced very promising results in the detecting the obstructive sleep apnea syndrome.
Journal of Medical Systems 10/2008; 32(5):379-87. · 1.13 Impact Factor
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[hide abstract]
ABSTRACT: Obstructive sleep apnea syndrome (OSAS) is an important disease that affects both the right and the left cardiac ventricle.
This paper presents a novel classification method called pairwise ANFIS based on Adaptive Neuro-Fuzzy Inference System (ANFIS)
and one against all method for detecting the obstructive sleep apnea syndrome. In order to extract the features related with
OSAS, we have used the clinical features obtained from Polysomnography device as a diagnostic tool for obstructive sleep apnea
(OSA) in patients clinically suspected of suffering from this disease. The clinical features obtained from Polysomnography
Reports are Arousals Index (ARI), Apnea and Hypoapnea Index (AHI), SaO2 minimum value in stage of REM, and Percent Sleep Time (PST) in stage of SaO2 intervals bigger than 89%. Since ANFIS has output with one class, we have extended the output of ANFIS to multi class by
means of one against all method to diagnose the OSAS that has four classes consisting of normal (25 subjects), mild OSAS (AHI = 5–15
and 14 subjects), middle OSAS (AHI < 15–30 and 18 subjects), and heavy OSAS (AHI > 30 and 26 subjects). The classification
accuracy, sensitivity and specifity analysis, mean square error, and confusion matrix have been used to test the performance
of proposed method. The obtained classification accuracies are 82.92%, 82.92%, 85.36%, and 87.80% for each class including
normal, mild OSAS, middle OSAS, and heavy OSAS using ANFIS with one against all method with 50–50% train-test split, respectively.
Combining ANFIS and one against all method that is firstly proposed by us was firstly applied for diagnosing the OSAS. The
proposed method has produced very promising results in the detecting the obstructive sleep apnea syndrome.
Journal of Medical Systems 09/2008; 32(5):379-387. · 1.13 Impact Factor
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ABSTRACT: The aorta diameter size one of the cardiac value is very important to guess for child before adult age, due to growing up
body. In conventional method, the experts use curve charts to decide whether their measured aortic diameter size is normal
or not. Our proposed method presents a valid virtual aortic diameter result related to age, weight and sex. The proposed method
comprises of two stages: (i) data normalization using a normalization method called Line Base Normalization Method (LBNM)
that is firstly proposed by us, (ii) normalized aortic diameter prediction using Adaptive Network Based Fuzzy Inference Systems
(ANFIS). Data set includes real Turkish infants, children and adolescents values and divided into two groups as 50% training
-50% testing split of whole dataset to show performance of ANFIS. LBNM compared to three normalization methods including Min-Max
normalization, Z-score, and decimal scaling methods. The results were compared to real aortic diameters values by expert with
nine year experiences in medical area.
09/2008: pages 498-505;
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ABSTRACT: This paper presents a new method based on combining principal component analysis (PCA) and adaptive network-based fuzzy inference system (ANFIS) to diagnose the optic nerve disease from visual-evoked potential (VEP) signals. The aim of this study is to improve the classification accuracy of ANFIS classifier on diagnosis of optic nerve disease from VEP signals. With this aim, a new classifier ensemble based on ANFIS and PCA is proposed.
The VEP signals dataset include 61 healthy subjects and 68 patients suffered from optic nerve disease. First of all, the dimension of VEP signals dataset with 63 features has been reduced to 4 features using PCA. After applying PCA, ANFIS trained using three different training-testing datasets randomly with 50-50% training-testing partition.
The obtained classification results from ANFIS trained separately with three different training-testing datasets are 96.87%, 98.43%, and 98.43%, respectively. And then the results of ANFIS trained with three different training-testing datasets randomly with 50-50% training-testing partition have been combined with three different ways including weighted arithmetical mean that proposed firstly by us, arithmetical mean, and geometrical mean. The classification results of ANFIS combined with three different ways are 98.43%, 100%, and 100%, respectively. Also, ensemble ANFIS has been compared with ANN ensemble. ANN ensemble obtained 98.43%, 100%, and 100% prediction accuracy with three different ways including arithmetical mean, geometrical mean and weighted arithmetical mean.
These results have shown that the proposed classifier ensemble approach based on ANFIS trained with different train-test datasets and PCA has produced very promising results in the diagnosis of optic nerve disease from VEP signals.
Artificial Intelligence in Medicine 07/2008; 43(2):141-9. · 1.35 Impact Factor
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ABSTRACT: 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, apneahypopnea index (AHI), SaO2 minimum value in stage of rapid eye movement, and percent sleep time in stage of SaO2 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.
Journal of Medical Systems 07/2008; 32(3):243-50. · 1.13 Impact Factor
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ABSTRACT: In this paper, we have proposed a novel similarity-based weighting method (SBWM), which combines similarity measure and weighting based on trend association (WBTA) method proposed by Sun Yi et al. (ICNN&B international conference, vol 1, pp 266-269, 2005). The aim of this study is to improve the classification accuracy of atherosclerosis, which is a common disease among the public. The proposed method consists of three parts: (1) feature extraction part related with atherosclerosis disease using fast Fourier transformation (FFT) modeling and calculation of maximum frequency envelope of sonograms, (2) data pre-processing part using SBWM, including different similarity measures such as cosine amplitude method, max-min method, absolute exponential method, and exponential similarity coefficient, and (3) classification part using artificial immune recognition system (AIRS) and Fuzzy-AIRS classifier algorithms. While AIRS and Fuzzy-AIRS algorithms obtained 71.92 and 78.94% success rates, respectively, the combination of SBWM with classifier algorithms including AIRS and Fuzzy-AIRS obtained 100% success rate on all the similarity measures. These results show that SBWM has produced very promising results in the classification of atherosclerosis from carotid artery Doppler signals. In future, we will use a larger dataset to test the proposed method.
Medical & Biological Engineering & Computing 04/2008; 46(4):353-62. · 1.88 Impact Factor
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ABSTRACT: In this study, we proposed a new medical diagnosis system based on principal component analysis (PCA), k-NN based weighting pre-processing, and Artificial Immune Recognition System (AIRS) for diagnosis of atherosclerosis from Carotid Artery Doppler Signals. The suggested system consists of four stages. First, in the feature extraction stage, we have obtained the features related with atherosclerosis disease using Fast Fourier Transformation (FFT) modeling and by calculating of maximum frequency envelope of sonograms. Second, in the dimensionality reduction stage, the 61 features of atherosclerosis disease have been reduced to 4 features using PCA. Third, in the pre-processing stage, we have weighted these 4 features using different values of k in a new weighting scheme based on k-NN based weighting pre-processing. Finally, in the classification stage, AIRS classifier has been used to classify subjects as healthy or having atherosclerosis. Hundred percent of classification accuracy has been obtained by the proposed system using 10-fold cross validation. This success shows that the proposed system is a robust and effective system in diagnosis of atherosclerosis disease.
Journal of Biomedical Informatics 03/2008; 41(1):15-23. · 1.79 Impact Factor
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ABSTRACT: In this paper, we have investigated the effect of generalized discriminate analysis (GDA) on classification performance of optic nerve disease from visual evoke potentials (VEP) signals. The GDA method has been used as a pre-processing step prior to the classification process of optic nerve disease. The proposed method consists of two parts. First, GDA has been used as pre-processing to increase the distinguishing of optic nerve disease from VEP signals. Second, we have used the C4.5 decision tree classifier, Levenberg Marquart (LM) back propagation algorithm, artificial immune recognition system (AIRS), linear discriminant analysis (LDA), and support vector machine (SVM) classifiers. Without GDA, we have obtained 84.37%, 93.75%, 75%, 76.56%, and 53.125% classification accuracies using C4.5 decision tree classifier, LM back propagation algorithm, AIRS, LDA, and SVM algorithms, respectively. With GDA, 93.75%, 93.86%, 81.25%, 93.75%, and 93.75% classification accuracies have been obtained using the above algorithms, respectively. These results show that the GDA pre-processing method has produced very promising results in diagnosis of optic nerve disease from VEP signals.
Computers in Biology and Medicine 02/2008; 38(1):62-8. · 1.09 Impact Factor
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ABSTRACT: 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), SaO2 minimum value in stage of rapid eye movement, and percent sleep time in stage of SaO2 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.
Journal of Medical Systems 01/2008; 32(3):243-250. · 1.13 Impact Factor
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ABSTRACT: 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.
Digital Signal Processing. 01/2008;
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ABSTRACT: ObjectiveData reduction methods are a crucial step affecting both performance and computation time of classification systems in pattern recognition applications such as medical decision making systems, intelligent control, and data clustering. The aim of this study is both to increase the classification accuracy and decrease the computation time of classifier system on the classification of epileptiform EEG signals.MethodsIn this study, we have proposed a novel data reduction method based on distances between groups data double in all dataset and applied this method to the classification of epileptiform EEG signals. The feature extraction methods including autoregressive (AR), discrete Fourier transform (DFT), and discrete wavelet transform (DWT), distance based data reduction, and C4.5 decision tree classifier have been combined to classify the epileptiform EEG signals. As feature extraction part AR, DFT, and DWT methods have been used to determine the features about EEG signals including epileptic seizure patients and eyes open volunteers. As data pre-processing part, distance based data reduction that is proposed firstly by us has been used to reduce data determined by spectral analysis methods (AR, DFT, and DWT). As final part called classification, C4.5 decision tree classifier has been used to classify reduced epileptiform EEG signals.ResultsTo validate and test the proposed data reduction, the classification accuracy, sensitivity, and specifity analysis, computation time, 10-fold cross-validation, and 95% confidence intervals have been used in this study. Six different combined methods have been used to classify the epileptiform EEG signal. These methods are (i) combining DFT and C4.5 decision tree classifier (DCT), (ii) combining DFT, distance based data reduction, and C4.5 DCT, (iii) combining AR and C4.5 DCT, (iv) combining AR, distance based data reduction, and C4.5 DCT, (v) combining DWT and C4.5 DCT, and (vi) combining DWT, distance based data reduction, and C4.5 DCT. The classification accuracies and computation times obtained by these methods are 99.02% – 79 s, 99.12% – 47 s, 99.32% – 65 s, 98.94% – 45 s, 92.00% – 52.06 s, and 89.50% – 29.9 s.ConclusionsThese results have shown that the proposed distance based data reduction method has produced very promising results with respect to both classification accuracy and computation time for classifying the epileptiform EEG signals. Also, proposed hybrid systems can be used to detect the epileptic seizure.
Applied Mathematics and Computation. 01/2008;
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ABSTRACT: Because of its self-regulating nature, immune system has been an inspiration source for usually unsupervised learning methods in classification applications of Artificial Immune Systems (AIS). But classification with supervision can bring some advantages to AIS like other classification systems. Indeed, there have been some studies, which have obtained reasonable results and include supervision in this branch of AIS. In this study, we have proposed a new supervised AIS named as Supervised Affinity Maturation Algorithm (SAMA) and have presented its performance results through applying it to diagnose atherosclerosis using carotid artery Doppler signals as a real-world medical classification problem. We have employed the maximum envelope of the carotid artery Doppler sonograms derived from Autoregressive (AR) method as an input of proposed classification system and reached a maximum average classification accuracy of 98.93% with 10-fold cross-validation method used in training-test portioning. To evaluate this result, comparison was done with Artificial Neural Networks and Decision Trees. Our system was found to be comparable with those systems, which are used effectively in literature with respect to classification accuracy and classification time. Effects of system's parameters were also analyzed in performance evaluation applications. With this study and other possible contributions to AIS, classification algorithms with effective performances can be developed and potential of AIS in classification can be further revealed.
Computer Methods and Programs in Biomedicine 01/2008; 88(3):246-55. · 1.52 Impact Factor
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ABSTRACT: Sleep disorders are a very common unawareness illness among public. Obstructive Sleep Apnea Syndrome (OSAS) is characterized
with decreased oxygen saturation level and repetitive upper respiratory tract obstruction episodes during full night sleep.
In the present study, we have proposed a novel data normalization method called Line Based Normalization Method (LBNM) to
evaluate OSAS using real data set obtained from Polysomnography device as a diagnostic tool in patients and clinically suspected
of suffering OSAS. Here, we have combined the LBNM and classification methods comprising C4.5 decision tree classifier and
Artificial Neural Network (ANN) to diagnose the OSAS. Firstly, each clinical feature in OSAS dataset is scaled by LBNM method
in the range of [0,1]. Secondly, normalized OSAS dataset is classified using different classifier algorithms including C4.5
decision tree classifier and ANN, respectively. The proposed normalization method was compared with min-max normalization,
z-score normalization, and decimal scaling methods existing in literature on the diagnosis of OSAS. LBNM has produced very
promising results on the assessing of OSAS. Also, this method could be applied to other biomedical datasets.
12/2007: pages 185-191;
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ABSTRACT: This paper presents a hybrid approach based on feature selection, fuzzy weighted pre-processing and artificial immune recognition system (AIRS) to medical decision support systems. We have used the heart disease and hepatitis disease datasets taken from UCI machine learning database as medical dataset. Artificial immune recognition system has shown an effective performance on several problems such as machine learning benchmark problems and medical classification problems like breast cancer, diabetes, and liver disorders classification. The proposed approach consists of three stages. In the first stage, the dimensions of heart disease and hepatitis disease datasets are reduced to 9 from 13 and 19 in the feature selection (FS) sub-program by means of C4.5 decision tree algorithm (CBA program), respectively. In the second stage, heart disease and hepatitis disease datasets are normalized in the range of [0,1] and are weighted via fuzzy weighted pre-processing. In the third stage, weighted input values obtained from fuzzy weighted pre-processing are classified using AIRS classifier system. The obtained classification accuracies of our system are 92.59% and 81.82% using 50-50% training-test split for heart disease and hepatitis disease datasets, respectively. With these results, the proposed method can be used in medical decision support systems.
Computer Methods and Programs in Biomedicine 12/2007; 88(2):164-74. · 1.52 Impact Factor
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ABSTRACT: In this paper, we have investigated the real-world task of recognizing biological concepts in DNA sequences. Recognizing promoters
in strings that represent nucleotides (one of A, G, T, or C) has been performed using a hybrid approach based on combining
feature selection (FS), fuzzy weighted pre-processing, and C4.5 decision tree classifier (DCS). Dimensionality of E.coli Promoter
Gene Sequences dataset has 57 attributes and 106 samples including 53 promoters and 53 non-promoters. The proposed approach
consists of three stages. Firstly, we have used the FS process to reduce the dimensionality of E.coli Promoter Gene Sequences
dataset that has 57 attributes. So the dimensionality of this dataset has been reduced to 4 attributes by means of FS process.
Secondly, fuzzy weighted pre-processing has been used to weight E.coli Promoter Gene Sequences dataset that has 4 attributes
in interval of [0,1]. Finally, C4.5 decision tree classifier algorithm has been run to estimation the E.coli Promoter Gene
Sequences. In order to show the performance of the proposed system, we have used the predicton accuracy and 10-fold cross
validation. 93.33% classification accuracy has been obtained by the proposed system using 10-fold cross validation. This success
shows that the proposed system is a robust and effective system in the prediction of E.coli Promoter Gene Sequences.
09/2007: pages 125-131;
