Effective diagnosis of coronary artery disease using the rotation forest ensemble method

PubMedID: 21912972Coronary Artery Disease is a common heart disease related to disorders effecting the heart and blood vessels. Since the disease is one of the leading causes of heart attacks and thus deaths, diagnosis of the disease in its early stages or in cases when patients do not show many of the symptoms yet has considerable importance. In the literature, studies based on computational methods have been proposed to diagnose the disease with readily available and easily collected patient data, and among these studies, the greatest accuracy reached is 89.01%. This paper presents a computational tool based on the Rotation Forest algorithm to effectively diagnose Coronary Artery Disease in order to support clinical decision-making processes. The proposed method utilizes Artificial Neural Networks with the Levenberg-Marquardt back propagation algorithm as base classifiers of the Rotation Forest ensemble method. In this scheme, 91.2% accuracy in diagnosing the disease is accomplished, which is, to the best of our knowledge, the best performance among the computational methods from the literature that use the same data. This paper also presents a comparison of the proposed method with some other classifiers in terms of diagnosis performance of Coronary Artery Disease. © 2011 Springer Science+Business Media, LLC

Diagnosis of Parkinson's disease by using neural networks ensemble [Birleştirilmiş yapay sinir aglariyla Parkinson hastaligi teşhisi]

2010 7th National Conference on Electrical, Electronics and Computer Engineering, ELECO 2010 --2 December 2010 through 5 December 2010 -- Bursa --Diagnosis of Parkinson's, a neurological disease, is hard specifically at its early stages. Thus, research on computer based solutions to support clinical decision making has increased recently. In this study, a new classifier method that is an ensemble of different existing classifiers is utilized to diagnose Parkinson's disease in its early stages. Underlying algorithms behind the ensemble approach are three neural networks with different learning schemes. These learning methods are Levenberg-Marquardt, Fletcher-Powell and Resilient back-propagation algorithms. When the new ensemble method is compared with the used neural network structures separately, it is observed that the new approach is superior to all existing methods. An accuracy of %96.9 is obtained with the ensemble method. The new approach proves itself as a promising method in computer-aided early diagnosis of Parkinson's disease

Recognition of non-speech sounds using Mel-frequency cepstrum coefficients and dynamic time warping method [MEL-FREKANS KEPSTRAL KATSAYILARI VE DINAMIK ZAMAN BÜKMESI YÖNTEMI KULLANILARAK KONUŞMA-DIŞI SESLERIN TANINMASI]

2015 23rd Signal Processing and Communications Applications Conference, SIU 2015 --16 May 2015 through 19 May 2015 -- --With the developing technology, speech recognition systems are getting more space in our daily lives. Sounds in our environment are not only pure speech. Because of this, it is important for cochlear implants, unmanned vehicles and security systems to be able to recognize other sounds. In this work, Mel-frequency cepstrum coefficients, one of the most widely used methods for feature extraction in speech recognition, applied to various nature and animal sounds. Because each sound does not have the same duration, dynamic time warping, one of the methods used in speech recognition, is preferred to classify the feature vectors. The difference in durations of sounds affects the lengths of the feature vectors. With dynamic time warping method, one can overcome these differences. One reference record and 10 test records obtained from 10 different sound sources. True classification rate is found as 88%. © 2015 IEEE

Assessment of Gaussian radial basis function network on protein secondary structures

IEEE23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society --25 October 2001 through 28 October 2001 -- Istanbul --Studies of the radial basis function (RBF) network on protein secondary structures are presented. Secondary structure prediction is a useful first step in understanding how the amino acid sequence of protein determines the native state. If the secondary structure is known, it is possible to derive a comparatively small number of tertiary structures using the secondary structural element pack. A study of the Gaussian-RBF with different window sizes on the dataset developed by Qian-Sejnowski, and also a dissimilar dataset by Chandonia is given. The RBF network predicts each position in turn based on a local window of residues, by sliding this window along the length of the sequence. It is shown that the Gaussian RBF network is not an appropriate technique to be used in the prediction of secondary structure for sequence structural state

Classification of Down Syndrome of Mice Protein Dataset on MongoDB Database

There are samples both with Down Syndrome and without in mice protein expression data set. It is important to define the reason of Down Syndrome treatment by means of mice protein for the same treatment seem human being. In the present study, mice protein expression data set from UCI repository are classified using Bayesian Network algorithm, K- Nearest Neighbor, Decision Table, Random Forest and Support Vector Machine which are some of classification methods. The classification algorithms with 10-fold cross validation and by splitting equally as test and train data are tested to classify on the mice protein data set. The classification of the data set was succeeded with 94.3519% accuracy in 0.06 seconds using Bayesian Network, with 99.2593% accuracy in 0.01 seconds using KNN, with 95.4630 % accuracy in 1.2 seconds using Decision Table, with 100% accuracy in 0.58 seconds using Random Forest and with 100% accuracy in 1.17 seconds using SVM, with 10-fold cross validation. On the other hand, the classification of the data set was succeeded with 95.3704% accuracy in 0.22 seconds using Bayesian Network, with 98.3333% accuracy in 0 seconds using KNN, with 98.3333% accuracy in 0.72 seconds using Decision Table, with 100% accuracy in 0.77 seconds using Random Forest and with 100% accuracy in 1.48 seconds using SVM, by equally train-test data partitio

Application of artificial neural networks to the prediction of critical buckling loads of helical compression springs

This paper proposes the use of artificial neural networks (ANN) to predict perfectly the critical buckling loads of cylindrical isotropic helical spring with fixed ends and with circular sections, and also having large pitch angles. The buckling equations of cylindrical isotropic helical springs loaded axially consist of a set of twelve linear differential equations. As finding a solution in an analytical manner is too difficult, they are solved numerically in an exact manner based on the transfer-matrix method to collect consistent dimensionless numerical data for the training process. Then almost perfect weight values are obtained to predict the non-dimensional buckling loads. A good agreement is observed with the data available in the literature. © 20xx Journal of Mechanical Engineering. All rights reserved

A hybrid image processing system for X-ray images of an external fixator

PubMedID: 21491259In the field of orthopaedics, treatment of extremity deformities can be realised by means of external fixators. However, control of such biomedical system is very difficult. Some different mathematical models have been developed to improve quality of this service. Most of the parameters, which are used in these models, have been obtained from two orthogonal X-ray images: one from anteroposterior, AP, direction and the other from a lateral, L, direction. The quality of the results of this model is dependent on the accuracy of the input parameters. Measuring these parameters is a time-consuming issue, and the accuracy of the results is also low. To increase the quality of the measurement, the reference points should be chosen from the edges of the biomedical system, and it is important to find the edges without noise. To achieve this purpose, Sobel edge detector, binary large object analysis, thresholding and inverting are applied as image processing steps. The results are compared with manual measurement values which have been obtained earlier. The results show that semi-automatic measurement of the parameters is more accurate and faster than manual measurement. It shows that the efficiency of the fixator method has been improved. © 2012 Copyright Taylor and Francis Group, LLC.106M466This study was financially supported by C¸ ukurova University under Grant No. MMF 2009 YL20 and partially by Turkish Research and Science Council (TÜBİTAK) under Grant No 106M466

New transform techniques for enhancement and fusion of multispectral and hyperspectral images

2005 ICSC Congress on Computational Intelligence Methods and Applications --15 December 2005 through 17 December 2005 -- Istanbul --This paper presents a new approach to the development of multispectral / hyperspectral image enhancement and fusion algorithms. In approaches used up to now for image enhancement, the bands are typically processed separately, and this results in considerable distortion. The amount of information in many bands are also not very efficiently used. The objective of this work is to utilize the amount of information available more effectively, remove such distortions and to improve the appearance of the images. To realize this goal, we developed two new algorithms using transform techniques. In the resulting algorithms developed, image enhancement and image fusion are considered together

The impact of imputation procedures with machine learning methods on the performance of classifiers: An application to coronary artery disease data including missing values

Prediction and learning in the presence of missing data are pervasive problems in data analysis by machine learning. This study focuses on the problems of collaborative classification with missing data on Coronary Artery Disease (CAD) and suggests alternative imputation methods in the case of the lack of laboratory test as well other specific parameters. This study develops three novel data imputation methods utilizing machine learning algorithms (K-means, Multilayer Perceptron (MLP), and Self-Organizing Maps (SOMs)) and compares the performance of our methods with well-known mean method. Benchmark classification methods (Logistic Model Trees (LMT), MLP, Random Forest (RF), and Support Vector Machine (SVM)) are used to conduct experiments on CAD dataset after imputation. The performance of the classifiers is evaluated according to the values of accuracy, specificity, sensitivity, f-measure, precision and normalized root mean square error. Based on statistical analysis, the SOM imputation method achieves the best values for accuracy (88.23%), F-measure (0.879), and precision (0.881). Moreover, MLP is mostly more stable than other imputation methods when the mean scores of the results of classifiers are considered. According to the results, the data imputation experiments conducted in this study suggests that machine learning imputation methods increase the prediction performance of the classifiers and strengthen disease-diagnosed success. © 2018, Scientific Publishers of India. All rights reserved.Mersin ÜniversitesiThe authors thank for the support of all physicians in the Department of Cardiology of Mersin University, Mersin, Turkey

Estimation and comparison of probabilistic temperatures through using artificial neural networks in geographic information systems media [Yapay Sinir Aglarıyla Cografi Bilgi Sistemi Ortamında Olasılıklı Sıcaklık Tahmini ve Karşılaştırılması]

The main objectives of this study are to develop the map of temperatures at 50% probability level through using Artificial Neural Networks method in Geographic Information System (GIS) Media and to compare GIS-based probabilistic temperatures of meteorological observation stations with the one produced by multiple regression technique in GIS media. This study was carried out in the Seyhan River Basin, covering 21,470.3 km2 surface area. Long-term (1975-2006) annual mean temperature series of 45 meteorological observation stations of Turkish State Meteorological Service (TSMS) were utilized in this study. Meteorological stations with the record length less than 15-year were determined and record length was extended to at least 15-year through using regression analysis. Then, frequency analysis was performed on the temperature series. Kolmogorov-Smirnov goodness-of-fit test was employed to determine whether the observed temperature values of a given meteorological station came from a particular, known, and completely specified cumulative probability distribution at the 5% significance level or not. Mean temperature values with 50% probability used in M.Turc surface runoff estimation method were estimated from probability distribution models for each meteorological station. Based on the "minimum error" criterion, mean temperature map at the 50% probability level, produced by artificial neural networks, was compared to the probability temperature map produced by multiple regression technique in GIS Media. It was concluded that temperatures estimated by Adaptive Liner Neuron (ADALINE) Network Model (RMSE=0.80) were more realistic results and close in GIS media to the observed temperatures in the basin, compared to the results obtained by Multiple Regression technique (RMSE=0.82) in GIS media. © Ankara Üniversitesi Ziraat Fakültesi