Attribute selection for stroke prediction

• Autorzy: Zdrodowska Małgorzata

Identyfikatory

DOI

10.2478/ama-2019-0026

• Języki publikacji: EN

Abstrakty

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.

Słowa kluczowe

EN

data mining; classifier; J48 (C4.5); CART; PART; naive Bayes classifier; random forest; support vector machine; multilayer perceptron; haemorrhagic stroke; ischemic stroke

• Wydawca: Oficyna Wydawnicza Politechniki Białostockiej
• Czasopismo: Acta Mechanica et Automatica
• Rocznik: 2019
• Tom: Vol. 13, no. 3
• Strony: 200--204
• Opis fizyczny: Bibliogr. 20 poz., tab., wykr.

Twórcy

autor

Zdrodowska Małgorzata

• Faculty of Mechanical Engineering, Department of Biocybernetics and Biomedical Engineering Bialystok Technical University, ul. Wiejska 45C, 15-351 Bialystok, Poland

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