Attribute Selection for Stroke Prediction

1. Aggarwal C.C. (2015), Data Classification Algorithms and Applications, Chapman & Hall/CRC, New York.Search in Google Scholar

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, 482–491.10.1007/978-3-319-67180-2_47Search in Google Scholar

3. Bramer M. (2016), Principles of Data Mining, Springer.10.1007/978-1-4471-7307-6Search in Google Scholar

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), 61-66.10.18178/ijmlc.2017.7.4.621Search in Google Scholar

5. Dardzińska A. (2013), Action Rules Mining, Springer, Berlin.10.1007/978-3-642-35650-6Search in Google Scholar

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), 181-188.10.1016/j.advms.2018.08.00930716648Search in Google Scholar

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.Search in Google Scholar

8. Han J., Kamber M. (2006), Data mining. Concepts and Techniques, 2^nd ed, Elsevier, San Francisco.Search in Google Scholar

9. Jacobs L.K., Sapers B.L. (2011), Neurological Disease, In: Cohn S. (editor), Perioperative Medicine, Springer, London.10.1007/978-0-85729-498-2_29Search in Google Scholar

10. Kasperczuk A., Daniluk J., Dardzińska A.(2019), Smart Model to Distinguish Crohn’s Disease from Ulcerative Colitis, Applied Sciences, 9(8), 1650.10.3390/app9081650Search in Google Scholar

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/s41601-018-0103-3.10.1186/s41601-018-0103-3Open DOISearch in Google Scholar

12. Mackay J., Mensah G. (2004), The Atlas of Heart Disease and Stroke: Global burden of stroke, World Health Organization.Search in Google Scholar

13. Maimon O., Rokach L. (ed). (2010), Data mining and knowledge discovery handbook, Springer.10.1007/978-0-387-09823-4Search in Google Scholar

14. Mazur R., Świerkocka-Miastkowska M. (2005), Stroke - first symptoms (in Polish), Choroby Serca i Naczyń, 2 (2), 84-87.Search in Google Scholar

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, 2064-2089.10.1161/STR.0b013e318296aeca23652265Search in Google Scholar

16. Strepikowska A., Buciński A. (2009), Stroke – risk factors and prophylaxis (in Polish), Farmakopea Polska, 65(1), 46–50.Search in Google Scholar

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), 44–54.10.15225/PNN.2017.6.2.1Search in Google Scholar

18. Witten I.H., Frank E., Hall M.A. (2011), Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann.Search in Google Scholar

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), 2431–2448.10.1007/s10916-011-9710-521537851Search in Google Scholar

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), 217-220.10.2478/ama-2018-0033Search in Google Scholar