Wyniki wyszukiwania - BazTech

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Comparison of selected classification methods based on machine learning as a diagnostic tool for knee joint cartilage damage based on generated vibroacoustic processes

Karpiński Robert, Krakowski Przemysław, Jonak Józef, Machrowska Anna, Maciejewski Marcin

Applied Computer Science

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2023

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Vol. 19, no 4

136--150

EN

Osteoarthritis is one of the most common cause of disability among elderly. It can affect every joint in human body, however, it is most prevalent in hip, knee, and hand joints. Early diagnosis of cartilage lesions is essential for fast and accurate treatment, which can prolong joint function. Available diagnostic methods include conventional X-ray, ultrasound and magnetic resonance imaging. However, those diagnostic modalities are not suitable for screening purposes. Vibroarthrography is proposed in literature as a screening method for cartilage lesions. However, exact method of signal acquisition as well as classification method is still not well established in literature. In this study, 84 patients were assessed, of whom 40 were in the control group and 44 in the study group. Cartilage status in the study group was evaluated during surgical treatment. Multilayer perceptron - MLP, radial basis function - RBF, support vector method - SVM and naive classifier – NBC were introduced in this study as classification protocols. Highest accuracy (0.893) was found when MLP was introduced, also RBF classification showed high sensitivity (0.822) and specificity (0.821). On the other hand, NBC showed lowest diagnostic accuracy reaching 0.702. In conclusion vibroarthrography presents a promising diagnostic modality for cartilage evaluation in clinical setting with the use of MLP and RBF classification methods.

2

Attribute selection for stroke prediction

Zdrodowska Małgorzata

Acta Mechanica et Automatica

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2019

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Vol. 13, no. 3

200--204

EN

Stroke is the third most common cause of death and the most common cause of long-term disability among adults around theworld. Therefore, stroke prediction and diagnosis is a very important issue. Data mining techniques come in handy to help determine the correlations between individual patient characterisation data, that is, extract from the medical information system the knowledge necessary to predict and treat various diseases. The study analysed the data of patients with stroke using eight known classification algorithms (J48 (C4.5), CART, PART, naive Bayes classifier, Random Forest, Supporting Vector Machine and neural networks Multilayer Perceptron), which allowed to build an exploration model given with an accuracy of over 88%. The potential features of patients, which may be factors that increase the risk of stroke, were also indicated.

3

Classification algorithms to identify changes in resistance

Duraj A., Korzeniewska E., Krawczyk A.

Przegląd Elektrotechniczny

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2015

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R. 91, nr 12

80-82

EN

In the article the basic method for measuring the resistance of medical electrodes, made based on a thin conductive layer formed during the PVD process, is described. The authors also briefly characterized two algorithms for data classification: k-nearest neighbors and Bayes classifier, which were used as algorithms to detect changes in the electrode resistance.

PL

W artykule została opisana podstawowa metoda pomiaru rezystancji elektrod medycznych wykonanych w oparciu o cienkie warstwy przewodzące powstałe w procesie PVD. Scharakteryzowano również krótko dwa algorytmy klasyfikacji danych: algorytm k najbliższych sąsiadów oraz klasyfikator bayowski, które zostały wykorzystane jako algorytmy identyfikacji zmian rezystancji elektrod.

4

Learning the naive Bayes classifier with optimization models

Taheri S., Mammadov M.

International Journal of Applied Mathematics and Computer Science

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2013

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Vol. 23, no. 4

787--795

EN

Naive Bayes is among the simplest probabilistic classifiers. It often performs surprisingly well in many real world applications, despite the strong assumption that all features are conditionally independent given the class. In the learning process of this classifier with the known structure, class probabilities and conditional probabilities are calculated using training data, and then values of these probabilities are used to classify new observations. In this paper, we introduce three novel optimization models for the naive Bayes classifier where both class probabilities and conditional probabilities are considered as variables. The values of these variables are found by solving the corresponding optimization problems. Numerical experiments are conducted on several real world binary classification data sets, where continuous features are discretized by applying three different methods. The performances of these models are compared with the naive Bayes classifier, tree augmented naive Bayes, the SVM, C4.5 and the nearest neighbor classifier. The obtained results demonstrate that the proposed models can significantly improve the performance of the naive Bayes classifier, yet at the same time maintain its simple structure.

5

Propozycja mieszanego przetwarzania półstrukturalnego modelu opisu zdarzeń z akcji ratowniczo-gaśniczych Państwowej Straży Pożarnej PSP3

Mirończuk M., Maciak T.

Bezpieczeństwo i Technika Pożarnicza

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2013

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Nr 1

95-106

PL

W opracowaniu przedstawiono aktualnie rozwijane reprezentacje wiedzy i sposoby opisów zdarzeń, dla systemu wnioskowania na podstawie przypadków zdarzeń służb ratowniczych Państwowej Straży Pożarnej PSP. W artykule zaproponowano sposób ich przetwarzania. Przedstawiony sposób bazuje na klasyfikacji i wyszukiwaniu opisów zdarzeń.

EN

This paper describes a review of actual developed knowledge representation and case representation for fire services cases based reasoning system. The article also describes a method of processing the cases of events. This processing method based on classification and information retrieval.