RESEARCH PAPER
Attribute Selection for Stroke Prediction
1
Faculty of Mechanical Engineering, Department of Biocybernetics and Biomedical Engineering, Bialystok Technical University, ul. Wiejska 45C, 15-351 Bialystok, Poland
Submission date: 2019-04-24
Acceptance date: 2019-09-30
Online publication date: 2019-11-05
Publication date: 2019-09-01
Acta Mechanica et Automatica 2019;13(3):200-204
KEYWORDS
data miningclassifierJ48 (C4.5)CARTPARTnaive Bayes classifierRandom ForestSupport Vector MachineMultilayer Perceptronhaemorrhagic strokeischaemic stroke
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.
REFERENCES (20)
1.
Aggarwal C.C. (2015), Data Classification Algorithms and Applications, Chapman & Hall/CRC, New York.
2.
Alaiz-Moreton H., Fernández-Robles L., Alfonso-Cendón J., Castejón-Limas M., Sánchez-González L., Pérez H. (2018),Data mining techniques for the estimation of variables in health-related noisy data, Advances in intelligent systems and computing, 649, 482–491.
4.
Chen Y.C., Suzuki T., Suzuki M., Takao H., Murayama Y., Ohwada H. (2017), Building a Classifier of Onset Stroke Prediction Using Random Tree Algorithm, International Journal of Machine Learning and Computing, 7(4), 61-66.
6.
Derlatka M., Ihnatouski M., Jałbrzykowski M., Lashkovski V., Minarowski Ł. (2019),Ensembling rules in automatic analysis of pressure on plantar surface in children with pes planovalgus, Advances in Medical Sciences, 64(1), 181-188.
7.
Frank E., Hall M.A., Witten I.A. (2016), The WEKA Workbench. Online Appendix for “Data Mining: Practical Machine Learning Tools and Techniques”, Morgan Kaufmann.
8.
Han J., Kamber M. (2006), Data mining. Concepts and Techniques, 2nd ed, Elsevier, San Francisco.
9.
Jacobs L.K., Sapers B.L. (2011), Neurological Disease, In: Cohn S. (editor), Perioperative Medicine, Springer, London.
10.
Kasperczuk A., Daniluk J., Dardzińska A. (2019), Smart Model to Distinguish Crohn’s Disease from Ulcerative Colitis, Applied Sciences, 9(8), 1650.
11.
Kiranmai S.A., Laxmi J.A. (2018), Data mining for classification of power quality problems using WEKA and the effect of attributes on classification accuracy, Protection and Control of Modern Power Systems, 3(29),https://doi.org/10.1186/s41601....
12.
Mackay J., Mensah G. (2004), The Atlas of Heart Disease and Stroke: Global burden of stroke, World Health Organization.
13.
Maimon O., Rokach L. (ed). (2010), Data mining and knowledge discovery handbook, Springer.
14.
Mazur R., Świerkocka-Miastkowska M. (2005), Stroke - first symptoms (in Polish), Choroby Serca i Naczyń, 2 (2), 84-87.
15.
Sacco R.L., Kasner S.E., Broderick J.P., Caplan L.R., Connors J.J., Culebras A., Elkind M.S., George M.G., Hamdan A.D., Higashida R.T., Hoh B.L., Janis L.S., Kase C.S., Kleindorfer D.O., Lee J.M., Moseley M.E., Peterson E.D., Turan T.N., Valderrama A.L., Vinters H.V. (2013), An updated definition of stroke for the 21st century: a statement for healthcare professionals from the American Heart Association/American Stroke Association, Stroke, 44, 2064-2089.
16.
Strepikowska A., Buciński A. (2009), Stroke – risk factors and prophylaxis (in Polish), Farmakopea Polska, 65(1), 46–50.
17.
Trochimczyk A., Chorąży M., Snarska K.K. (2017), An analysis of patient quality of life after ischemic stroke of the brain, The journal of neurological and neurosurgical nursing, 6(2), 44–54.
18.
Witten I.H., Frank E., Hall M.A. (2011), Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann.
19.
Yoo I., Alafaireet P., Marinov M. (2012), Data mining in healtcare and biomedicine, A survey of the literature, Journal of the medical systems, 35(4), 2431–2448.
20.
Zdrodowska M., Dardzińska M., Chorąży M., Kułakowska A. (2018), Data Mining Techniques as a Tool in Neurological Disorders Diagnosis, Acta Mechanica et Automatica, 12(3), 217-220.
| eISSN: | 2300-5319 |
| ISSN: | 1898-4088 |
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
