A decision support system based on support vector machines for diagnosis of the heart valve diseases

Çomak, Emre; Arslan, Ahmet; Türkoğlu, İ̇brahim

doi:10.1016/j.compbiomed.2005.11.002

Cited by 143 publications

(59 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…SVM is a supervised learning algorithm, which is recommended for classification and nonlinear function approaches. More information about SVM can be obtained from Ç omak et al [22] and Vapnik et al…”

Section: Support Vector Machinesmentioning

confidence: 99%

Bone age determination in young children (newborn to 6 years old) using support vector machines

Güraksın¹,

Uğuz²,

Baykan³

2016

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

Abstract:Bone age is assessed through a radiological analysis of the left-hand wrist and is then compared to chronological age. A conflict between these two values indicates an abnormality in the development process of the skeleton. This study, conducted on children aged between 0 and 6 years, proposes a computer-based diagnostic system to eliminate the disadvantages of the methods used in bone age determination. For this purpose, primarily an image processing procedure was applied to the X-ray images of the left-hand wrist of children from different ethnic groups aged between 0 and 6 years. A total of 9 features, corresponding to the carpal bones and distal epiphysis of the radius bone with some physiological attributes of the children, were obtained. Then, by using gain ratio, the best 6 features were used for the classification process. Next, the bone age determination process was performed with the obtained features with the help of the support vector machine (SVM), naive Bayes, k-nearest neighborhood, and C4.5 algorithms. Finally, the features used in the determination process and their effects on the accuracies were examined. The results of the designed system showed that SVM method has a better achievement rate than the other methods at a rate of 72.82%. Additionally, in this study, a new feature corresponding to the distance between the centers of gravity of the carpal bones was used for the classification process, and the analysis of the related feature showed that there was a statistically significant difference at P <0.05 between this feature and bones in children aged between 0 and 6 years.

show abstract

Section: Support Vector Machinesmentioning

confidence: 99%

Bone age determination in young children (newborn to 6 years old) using support vector machines

Güraksın¹,

Uğuz²,

Baykan³

2016

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

show abstract

“…21 The SVM classifies data into two categories of membership by constructing a high dimensional hyperplane, while being optimized to maximize the margin that best divides membership. For the present study, an SVM model was developed using LibSVM (http://weka.wikispaces.com/ LibSVM) with the radial basis function as a kernel function.…”

Section: Support Vector Machine Modelmentioning

confidence: 99%

Development of a cervical cancer progress prediction tool for human papillomavirus-positive Koreans: A support vector machine-based approach

Kahng

Kim

et al. 2015

J Int Med Res

View full text Add to dashboard Cite

Objectives: To develop a Web-based tool to draw attention to patients positive for human papillomavirus (HPV) who have a high risk of progression to cervical cancer, in order to increase compliance with follow-up examinations and facilitate good doctor-patient communication. Methods: Records were retrospectively analysed from women who were positive for HPV on initial testing (before any treatment). Information concerning age, Papanicolaou (PAP) smear result and presence of 15 high-risk HPV genotypes was used in a support vector machine (SVM) model, to identify the patient features that maximally contributed to progression to high-risk cervical lesions. Results: Data from 731 subjects were analysed. The maximum number of correct cancer predictions was seen when four features (PAP, HPV16, HPV52 and HPV35) were used, giving an accuracy of 74.41%. A web-based high-risk cervical lesion prediction application tool was developed using the SVM model results. Conclusions: Use of the web-based prediction tool may help to increase patient compliance with physician advice, and may heighten awareness of the significance of regular follow-up HPV examinations for the prevention of cervical cancer, in Korean women predicted to have heightened risk of the disease.

show abstract

“…A good number of research works have been done recently using SVM. Emre et al [15] presented a decision support system that classifies the Doppler signals of the heart valve to two classes (normal and abnormal) by using Least-Squares Support Vector Machine (LS-SVM) classifier instead of Artificial Neural Network (ANN). The paper used a previous work where ANN was used as a classifier, as feature extractor from measured Doppler signal and concluded that LS-SVM has more advantage than ANN classifier especially in terms of training running time.…”

Section: Literature Reviewmentioning

confidence: 99%

Data Mining in Identifying Premium and Regular Gasoline Using Support Vector Machines as Novel Approach for Arson and Fuel Spill Investigation

Olatunji¹,

Adeleke²

2010

Int. Jour. Info. Sci. Comp.

View full text Add to dashboard Cite

In this work, a novel data mining model based on Support Vector Machines (SVM) for the identification of gasoline types has been investigated and developed. Detection and correct identification of gasoline types during Arson and Fuel Spill Investigation are very important in forensic science. As the number of arson and spillage becomes a common place, it becomes more important to have an accurate means of detecting and classifying gasoline found at such sites of incidence. However, currently only a very few number of classification models have been explored in this germane field of forensic science, particularly as relates to gasoline identification. Thus, we have developed Support Vector Machines (SVM) based identification model for identifying gasoline types. The model was constructed using gas chromatography-mass spectrometry (GC-MS) spectral data obtained from gasoline sold in Canada over one calendar year. Prediction accuracy of the model was evaluated and compared with earlier used methods on the same datasets. Empirical results from simulation showed that SVM based model produced accurate and promising results better than the best among the other earlier implemented Artificial Neural Network and Principal Component Analysis methods on the same datasets.

show abstract

A decision support system based on support vector machines for diagnosis of the heart valve diseases

Cited by 143 publications

References 16 publications

Bone age determination in young children (newborn to 6 years old) using support vector machines

Bone age determination in young children (newborn to 6 years old) using support vector machines

Development of a cervical cancer progress prediction tool for human papillomavirus-positive Koreans: A support vector machine-based approach

Data Mining in Identifying Premium and Regular Gasoline Using Support Vector Machines as Novel Approach for Arson and Fuel Spill Investigation

Contact Info

Product

Resources

About