Ibrahim Turkoglu

Effective diagnosis of heart disease through neural networks ensembles

In the last decades, several tools and various methodologies have been proposed by the researchers for developing effective medical decision support systems. Moreover, new methodologies and new tools are continued to develop and represent day by day. Diagnosing of the heart disease is one of the important issue and many researchers investigated to develop intelligent medical decision support systems to improve the ability of the physicians. In this paper, we introduce a methodology which uses SAS base software 9.1.3 for diagnosing of the heart disease. A neural networks ensemble method is in the centre of the proposed system. This ensemble based methods creates new models by combining the posterior probabilities or the predicted values from multiple predecessor models. So, more effective models can be created. We performed experiments with the proposed tool. We obtained 89.01% classification accuracy from the experiments made on the data taken from Cleveland heart disease database. We also obtained 80.95% and 95.91% sensitivity and specificity values, respectively, in heart disease diagnosis.

An expert system for diagnosis of the heart valve diseases

Abstract In this paper, an expert diagnosis system is presented for interpretation of the Doppler signals of the heart valve diseases based on the pattern recognition. This paper especially deals with the feature extraction from measured Doppler signal waveforms at the heart valve using the Doppler Ultrasound. Wavelet transforms and short time Fourier transform methods are used to feature extract from the Doppler signals on the time–frequency domain. Wavelet entropy method is applied to these features. The back-propagation neural network is used to classify the extracted features. The performance of the developed system has been evaluated in 215 samples. The test results showed that this system was effective to detect Doppler heart sounds. The correct classification rate was about 94% for normal subjects and 95.9% for abnormal subjects.

An intelligent system for diagnosis of the heart valve diseases with wavelet packet neural networks

In this paper, an intelligent system is presented for interpretation of the Doppler signals of the heart valve diseases based on the pattern recognition. This paper especially deals with combination of the feature extraction and classification from measured Doppler signal waveforms at the heart valve using the Doppler Ultrasound. Because of this, a wavelet packet neural network model developed by us is used. The model consists of two layers: wavelet and multi-layer perceptron. The wavelet layer is used for adaptive feature extraction in the time-frequency domain and is composed of wavelet packet decomposition and wavelet packet entropy. The multi-layer perceptron used for classification is a feed-forward neural network. The performance of the developed system has been evaluated in 215 samples. The test results showed that this system was effective in detecting Doppler heart sounds. The correct classification rate was about 94% for abnormal and normal subjects.

Wavelet packet neural networks for texture classification

Abstract Texture can be defined as a local statistical pattern of texture primitives in observer’s domain of interest. Texture classification aims to assign texture labels to unknown textures, according to training samples and classification rules. This paper describes the usage of wavelet packet neural networks (WPNN) for texture classification problem. The proposed schema composed of a wavelet packet feature extractor and a multi-layer perceptron classifier. Entropy and energy features are integrated wavelet feature extractor. The performed experimental studies show the effectiveness of the WPNN structure. The overall success rate is about 95%.

Intelligent target recognition based on wavelet packet neural network

In this paper, an intelligent target recognition system is presented for target recognition from target echo signal of High Resolution Range (HRR) radars. This paper especially deals with combination of the feature extraction and classification from measured real target echo signal waveforms using X-band pulse radar. Because of this, a wavelet packet neural network model developed by us is used. The model consists of two layers: wavelet and multi-layer perceptron. The wavelet layer is used for adaptive feature extraction in the time-frequency domain and is composed of wavelet packet decomposition and wavelet entropy. The multi-layer perceptron used for classification is a feed-forward neural network. The performance of the developed system has been evaluated in noisy radar target echo (RTE) signals. The test results showed that this system was effective in detecting real RTE signals. The correct classification rate was about 95% for used target subjects.

Prediction of wastewater treatment plant performance based on wavelet packet decomposition and neural networks

In this paper, an intelligent wastewater treatment plant model is developed to predict the performance of a wastewater treatment plant (WWTP). The developed model is based on wavelet packet decomposition, entropy and neural network. The data used in this work were obtained from a WWTP in Malatya, Turkey. Daily records of these WWTP parameters over a year were obtained from the plant laboratory. Wavelet packet decomposition was used to reduce the input vectors dimensions of intelligent model. The suitable architecture of the neural network model is determined after several trial and error steps. Total suspended solid is one of the measures of overall plant performance so the developed model is used to predict the total suspended solid concentration in plant effluent. According to test results, the developed model performance is at desirable level. This model is an efficient and a robust tool to predict WWTP performance.

Diagnosis of valvular heart disease through neural networks ensembles

In the last decades, several tools and various methodologies have been proposed by the researchers for developing effective medical decision support systems. Moreover, new methodologies and new tools are continued to develop and represent day by day. Diagnosing of the valvular heart disease is one of the important issue and many researchers investigated to develop intelligent medical decision support systems to improve the ability of the physicians. In this paper, we introduce a methodology which uses SAS Base Software 9.1.3 for diagnosing of the valvular heart disease. A neural networks ensemble method is in the centre of the proposed system. The ensemble-based methods creates new models by combining the posterior probabilities or the predicted values from multiple predecessor models. So, more effective models can be created. We performed experiments with proposed tool. We obtained 97.4% classification accuracy from the experiments made on data set containing 215 samples. We also obtained 100% and 96% sensitivity and specificity values, respectively, in valvular heart disease diagnosis.

A biomedical system based on hidden Markov model for diagnosis of the heart valve diseases

In this study, a biomedical diagnosis system for pattern recognition with normal and abnormal classes has been developed. First, feature extraction processing was made by using the Doppler Ultrasound. During feature extraction stage, Wavelet transforms and short-time Fourier transform were used. As next step, wavelet entropy were applied to these features. In the classification stage, hidden Markov model (HMM) was used. To compute the correct classification rate of proposed HMM classifier, it was compared to ANN by using a data set containing 215 samples. In our experiments, specificity rate and sensitivity rates of proposed HMM classifier system with fuzzy C means (FCM)/K-means algorithms were found as 92% and 97.26% respectively. The present study shows that proper selection of the HMMs initial parameter values according to FCM/K-means algorithms improves the recognition rate of the proposed system which was also compared to our previous study named ANN.

An intelligent diagnosis system based on principle component analysis and ANFIS for the heart valve diseases

In this paper, an intelligent diagnosis system based on principle component analysis (PCA) and adaptive network based on fuzzy inference system (ANFIS) for the heart valve disease is introduced. This intelligent system deals with combination of the feature extraction and classification from measured Doppler signal waveforms at the heart valve using the Doppler ultrasound (DHS). Here, the wavelet entropy is used as features. This intelligent system has three phases. In pre-processing phase, the data acquisition and pre-processing for DHS signals are performed. In feature extraction phase, the feature vector is extracted by calculating the 12 wavelet entropy values for per DHS signal and dimension of Doppler signal dataset, which are 12 features, is reduced to 6 features using PCA. In classification phase, these reduced wavelet entropy features are given to inputs ANFIS classifier. The correct diagnosis performance of the PCA-ANFIS intelligent system is calculated in 215 samples. The classification accuracy of this PCA-ANFIS intelligent system was 96% for normal subjects and 93.1% for abnormal subjects.

Creating meaningful data from web logs for improving the impressiveness of a website by using path analysis method

Web usage mining is to analyze web log files to discover user accessing patterns of web pages. In order to effectively manage and report on a website, it is necessary to get feedback about activity on the web servers. The aim of this study is to help the web designer and web administrator to improve the impressiveness of a website by determining occurred link connections on the website. Therefore, web log files are pre-processed and then path analysis technique is used to investigate the URL information concerning access to electronic sources. The proposed methodology is applied to the web log files in the web server of Firat University. The results and findings of this experimental study can be used by the web designer in order to plan the upgrading and enhancement to the website.