Attribute Selection for Stroke Prediction

Małgorzata Zdrodowska

Abstract

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.
Author Małgorzata Zdrodowska (FME)
Małgorzata Zdrodowska,,
- Faculty of Mechanical Engineering
Journal seriesActa Mechanica et Automatica, ISSN 1898-4088, e-ISSN 2300-5319, (N/A 40 pkt)
Issue year2019
Vol13
No3
Pages200-204
Publication size in sheets0.5
Keywords in Englishdata mining, classifier, J48 (C4.5), CART, PART, naive Bayes classifier, Random Forest, Support Vector Machine, Multilayer Perceptron, haemorrhagic stroke, ischaemic stroke
ASJC Classification2207 Control and Systems Engineering; 2210 Mechanical Engineering
DOIDOI:10.2478/ama-2019-0026
URL http://www.acta.mechanica.pb.edu.pl/volume/vol13no3/26_2019_023_ZDRODOWSKA.pdf
Internal identifierROC 19-20
Languageen angielski
LicenseJournal (articles only); published final; ; with publication
Score (nominal)40
Score sourcejournalList
ScoreMinisterial score = 40.0, 12-02-2020, ArticleFromJournal
Publication indicators Scopus SNIP (Source Normalised Impact per Paper): 2018 = 0.615
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