Support Vector Machine Ensembles for Intelligent Diagnosis of Valvular Heart Disease

Şengür, Abdulkadir

doi:10.1007/s10916-011-9740-z

Cited by 29 publications

(13 citation statements)

References 21 publications

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“…Predicting the amount of electricity produced in a power plant is very important for today's economy. To date, there are many fi eld work for classifi cation or clustering [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28].…”

Section: Resultsmentioning

confidence: 99%

Classification by Artificial Neural Network according to the values affecting Electricity Generation

Korkmaz¹

2018

Trends Comput Sci Inf Technol

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Predicting the amount of electricity produced in a power plant is very important for today's economy. Oven Power (MW), Boiler Input Gas Temperature, Superheated Steam Amount, ID-Fan Speed, Feeding Water Tank data affect the electricity production. In this article, Etikrom A.Ş. The electricity production amount to be produced in Elazığ Etikrom A.Ş. was estimated by using the data of Oven Power (MW), Water Inlet Gas Temperature, Steam Vapor Volume, ID-Fan Speed, Feeding Water Tank data. Electricity generation amount is used as verifi cation data. That is, by the k-means clustering method, the electricity generation amount is divided into 3 classes (low, medium, and high). 3621 data including Oven Power (MW), Boiler Input Gas Temperature, Superheated Steam Amount, ID-Fan Speed, and Feeding Water Tank data were used after class 3 separation. With the K-means clustering method, 2742 of these data were clustered as low electricity, 296 as medium electricity and 583 as high electricity. This clustered data was given to the Artifi cal Neural Network classifi er. The success rate obtained as a result of this classifi cation is 85.81%. Classifi ed data were analyzed by ROC curve.

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Section: Resultsmentioning

confidence: 99%

Classification by Artificial Neural Network according to the values affecting Electricity Generation

Korkmaz¹

2018

Trends Comput Sci Inf Technol

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“…All the data analysis and drawing figures were performed with R software (version 2.14.0) (R Development Core Team 2011); the LDA was implemented by the MASS (version 7.3-16) R package; the SVM and NBC functions were implemented by the e1071 (version 1.6) R package (Dimitriadou et al 2011); and the DT functions were implemented by the mvpart (version 1.4-0) R package. In addition, each classifier was performed with default parameter settings.…”

Section: Methodsmentioning

confidence: 99%

An approach to predict the risk of glaucoma development by integrating different attribute data

et al. 2012

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Primary open-angle glaucoma (POAG) is one of the major causes of blindness worldwide and considered to be influenced by inherited and environmental factors. Recently, we demonstrated a genome-wide association study for the susceptibility to POAG by comparing patients and controls. In addition, the serum cytokine levels, which are affected by environmental and postnatal factors, could be also obtained in patients as well as in controls, simultaneously. Here, in order to predict the effective diagnosis of POAG, we developed an “integration approach” using different attribute data which were integrated simply with several machine learning methods and random sampling. Two data sets were prepared for this study. The one is the “training data set”, which consisted of 42 POAG and 42 controls. The other is the “test data set” consisted of 73 POAG and 52 controls. We first examined for genotype and cytokine data using the training data set with general machine learning methods. After the integration approach was applied, we obtained the stable accuracy, using the support vector machine method with the radial basis function. Although our approach was based on well-known machine learning methods and a simple process, we demonstrated that the integration with two kinds of attributes, genotype and cytokines, was effective and helpful in diagnostic prediction of POAG.

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“…Ortiz et al 30 used neural networks to analyze cardiac contractility to predict 1-year mortality in patients with heart failure. Since this early work, supervised machine learning 26 RV, LV endocardium and epicardium CNN Tan et al 27 LV segmentation ANN Baessler et al 28 Myocardial scar detection Random forests Dawes et al 29 Pulmonary hypertension prognosis PCA ECHO Ortiz et al 30 HF prognosis ANN Narula et al 31 HCM vs athlete's heart SVM, Random forests, ANN Sengupta et al 32 Constrictive pericarditis vs restrictive cardiomyopathy AMC, random forest, k-NN, SVM Sengur 33 Valvular disease SVM Moghaddasi and Nourian 34 MR severity SVM Vidya et al 35 MI detection SVM CT Wolterink et al 36 CAC scoring CNN Isgum et al 37 CAC scoring k-NN, SVM Itu et al 38 FFR estimation deep neural network Motwani et al 39 Prognosis Logistic regression Mannil et al 40 MI detection Decision tree, k-NN, random forest, ANN 32 diagnose valvular heart disease, 33 grade severity of mitral valve regurgitation, 34 automate ejection fraction measurement, 53 and detect the presence of myocardial infarction. 35,54 Several machine learning applications have also been developed to assist in the interpretation of CT. For example, algorithms have been developed for the automation of coronary artery calcium scoring 36,37,55,56 and assessment of the functional significance of coronary lesions.…”

Section: Applications To Cardiovascular Diseasementioning

confidence: 99%

Machine Learning and Deep Neural Networks in Thoracic and Cardiovascular Imaging

Retson

Besser

Sall³

et al. 2019

Journal of Thoracic Imaging

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Advances in technology have always had the potential and opportunity to shape the practice of medicine, and in no medical specialty has technology been more rapidly embraced and adopted than radiology. Machine learning and deep neural networks promise to transform the practice of medicine, and, in particular, the practice of diagnostic radiology. These technologies are evolving at a rapid pace due to innovations in computational hardware and novel neural network architectures. Several cutting-edge postprocessing analysis applications are actively being developed in the fields of thoracic and cardiovascular imaging, including applications for lesion detection and characterization, lung parenchymal characterization, coronary artery assessment, cardiac volumetry and function, and anatomic localization. Cardiothoracic and cardiovascular imaging lies at the technological forefront of radiology due to a confluence of technical advances. Enhanced equipment has enabled computed tomography and magnetic resonance imaging scanners that can safely capture images that freeze the motion of the heart to exquisitely delineate fine anatomic structures. Computing hardware developments have enabled an explosion in computational capabilities and in data storage. Progress in software and fluid mechanical models is enabling complex 3D and 4D reconstructions to not only visualize and assess the dynamic motion of the heart, but also quantify its blood flow and hemodynamics. And now, innovations in machine learning, particularly in the form of deep neural networks, are enabling us to leverage the increasingly massive data repositories that are prevalent in the field. Here, we discuss developments in machine learning techniques and deep neural networks to highlight their likely role in future radiologic practice, both in and outside of image interpretation and analysis. We discuss the concepts of validation, generalizability, and clinical utility, as they pertain to this and other new technologies, and we reflect upon the opportunities and challenges of bringing these into daily use.

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Support Vector Machine Ensembles for Intelligent Diagnosis of Valvular Heart Disease

Cited by 29 publications

References 21 publications

Classification by Artificial Neural Network according to the values affecting Electricity Generation

Classification by Artificial Neural Network according to the values affecting Electricity Generation

An approach to predict the risk of glaucoma development by integrating different attribute data

Machine Learning and Deep Neural Networks in Thoracic and Cardiovascular Imaging

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