Formation features for improving the quality of medical diagnosis based on the discriminant analysis methods

Ilyasova, Nataly; Kupriyanov, Alexander; Paringer, Rustam

doi:10.18287/0134-2452-2014-38-4-851-855

Cited by 38 publications

(10 citation statements)

References 3 publications

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“…The group of the formed features is to be selected, based on the value of the intragroup partibility criterion. The exhaustive search of all possible combinations of features guarantees the best division in the definite classification problem [3].…”

Section: Methodsmentioning

confidence: 99%

“…The features that evaluate geometric characteristics of dendritic crystallograms have been developed [1,2]. To evaluate the efficiency of the developed features, for a classification problem of the presented class of on-scale images, a respective algorithm has been developed, based on the discriminant analysis algorithm [3]. The developed algorithm also enables to create spaces of new features ensuring the best possible separation in the definite classification problem [4,5].…”

Section: Introductionmentioning

confidence: 99%

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Dendritic crystallogram images classification

Paringer

Kupriyanov

Ilyasova

2015

JBPE

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Abstract.A computer classification system of dendritic crystallogram images is presented in this paper. To improve the quality of classification we use an algorithm for the informative features formation, using methods of discriminant analysis. The method for receiving an informativeness estimation was used. As basic features are seven geometric characteristic was calculated. The research confirming the efficiency of the formed features for classification of dendritic crystallogramms images was conducted by means of classification by support vector machine. Of these, was selected most informative basic five features and one new feature was formed. The error classification decreased from 0.081 to 0.061. The algorithm possesses a sufficient level of universality and may be applied to increase the informativeness of any feature set. © 2015 Samara State Aerospace University (SSAU).

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dendritic crystallogram images classification

Paringer

Kupriyanov

Ilyasova

2015

JBPE

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“…The accuracy, sensitivity and specificity of this biomarker were 83 %, 80% and 84%, respectively. For the selection of a small number of features one can use the brute force [2]. In biomedical data mining the sequential search algorithms [3] and genetic algorithms [4] are also used.…”

Section: Introductionmentioning

confidence: 99%

Feature Selection in the Effectiveness Research of a Training Program for Patients With the Atrial Fibrillation

Kutikova¹,

Gaidel

Khramov

2016

Collection of Selected Papers of the II International Conference on Information Technology and Nanotechnology

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Abstract. We investigated training therapeutic program effectiveness of the school "Stop a Stroke", which aimed at reducing a risk of a stroke for patients with the atrial fibrillation. On the basis of two feature selection methods using a criterion of the discriminant analysis to determine the best feature subset, we concluded that patients, who trained at the school, in contrast to patients, who have not received training, take anticoagulants for a long time and have a higher level of knowledge about the atrial fibrillation.Keywords: data mining, feature selection, discriminant analysis criterion.Citation: Kutikova VV, Gaidel AV, Khramov AG. Feature selection in the effectiveness research of a training program for patients with the atrial fibrillation. CEUR Workshop Proceedings, 2016; 1638 : 902-908. DOI: 10.18287/1613 -0073-2016 -1638 IntroductionReducing the dimensionality of a feature space is one of the central issues in data mining. For the most classification and recovery regression problems it is necessary to select the best subset from a given feature set. This is because the use of a large feature number is not only computational expensive, but also affects the recognition accuracy, as irrelevant and redundant features, which complicate the decision-making process, can be used. Feature selection methods are commonly used for the biomedical data analysis. In the work [1] using the method based on the ANCOVA an 80-gene biomarker of the smokers lung cancer was identified from 22216 features describing the expression levels of different genes. The accuracy, sensitivity and specificity of this biomarker were 83 %, 80% and 84%, respectively. For the selection of a small number of features one can use the brute force [2]. In biomedical data mining the sequential search algorithms [3] and genetic algorithms [4] are also used. Feature selection methods based on a criterion of the discriminant analysis have showed their effectiveness in [5,6] for the analysis of biomedical images.

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“…They are the genetic algo rithm [13] and the differential evolution method [14]. The discriminant analysis was already used by Rus sian scientists for the reference features formation when solving the medical diagnostic problems [15]. To describe this image we use a number of groups of the informational features.…”

Section: Introductionmentioning

confidence: 99%

Possibilities of texture analysis of computed tomogram in diagnosis of chronic obstructive disease

Gaidel

Zelter

Kapishnikov

et al. 2015

Opt. Mem. Neural Networks

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We analyzed the possibility to use different texture features for determination of the lung pathologic changes by examining two dimensional computed tomogram images. As informational features, we used the histogram and covariance characteristics as well as the Haralick features and the run length features. The effective features were selected basing on the discriminant analysis criterion. The obtained group of effective features provides only the part of 0.11 incorrect recognitions, which is better than the results obtained without using the procedure of the feature's selection.

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Formation features for improving the quality of medical diagnosis based on the discriminant analysis methods

Cited by 38 publications

References 3 publications

Dendritic crystallogram images classification

Dendritic crystallogram images classification

Feature Selection in the Effectiveness Research of a Training Program for Patients With the Atrial Fibrillation

Possibilities of texture analysis of computed tomogram in diagnosis of chronic obstructive disease

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