<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></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.

Attribute Selection for Stroke Prediction

Faculty of Mechanical Engineering, Department of Biocybernetics and Biomedical Engineering

Acta Mechanica et Automatica

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Attribute Selection for Stroke Prediction"}

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...

Attribute Selection for Stroke Prediction

Data publikacji: 05 lis 2019

Zakres stron: 200 - 204

Otrzymano: 24 kwi 2019

Przyjęty: 30 wrz 2019

DOI: https://doi.org/10.2478/ama-2019-0026

Słowa kluczowe
data mining, classifier, J48 (C4.5), CART, PART, naive Bayes classifier, Random Forest, Support Vector Machine, Multilayer Perceptron, haemorrhagic stroke, ischaemic stroke

© 2019 Małgorzata Zdrodowska, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Attribute Selection for Stroke Prediction

Data publikacji: 05 lis 2019

Zakres stron: 200 - 204

Otrzymano: 24 kwi 2019

Przyjęty: 30 wrz 2019

DOI: https://doi.org/10.2478/ama-2019-0026

Słowa kluczowedata mining, classifier, J48 (C4.5), CART, PART, naive Bayes classifier, Random Forest, Support Vector Machine, Multilayer Perceptron, haemorrhagic stroke, ischaemic stroke

© 2019 Małgorzata Zdrodowska, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Słowa kluczowe
data mining, classifier, J48 (C4.5), CART, PART, naive Bayes classifier, Random Forest, Support Vector Machine, Multilayer Perceptron, haemorrhagic stroke, ischaemic stroke