Title of article
Prediction and Control of Stroke by Data Mining
Author/Authors
Amini, Leila payame noor university - Department of Computer Engineering and Information Technology, تهران, ايران , Azarpazhouh, Reza mashhad university of medical sciences - Department of Neurology, ايران , Farzadfar, Mohammad Taghi mashhad university of medical sciences - Department of Neurology, ايران , Mousavi, Ali isfahan university of medical sciences - Isfahan Neurosciences Research Center - Department of Neurology, ايران , Jazaieri, Farahnaz tehran university of medical sciences tums - School of Medicine - Department of Pharmacology, تهران, ايران , Khorvash, Fariborz isfahan university of medical sciences - Isfahan Neurosciences Research Center - Department of Neurology, ايران , Norouzi, Rasul isfahan university of medical sciences - Isfahan Neurosciences Research Center - Department of Neurology, ايران , Toghianifar, Nafiseh isfahan university of medical sciences - Isfahan Neurosciences Research Center - Department of Neurology, ايران
From page
245
To page
249
Abstract
Background: Today there are abounding collected data in cases of various diseases in medical sciences. Physicians can access new findings about diseases and procedures in dealing with them by probing these data. This study was performed to predict stroke incidence. Methods: This study was carried out in Esfahan Al‑Zahra and Mashhad Ghaem hospitals during 2010‑2011. Information on 807 healthy and sick subjects was collected using a standard checklist that contains 50 risk factors for stroke such as history of cardiovascular disease, diabetes, hyperlipidemia, smoking and alcohol consumption. For analyzing data we used data mining techniques, K‑nearest neighbor and C4.5 decision tree using WEKA. Results: The accuracy of the C4.5 decision tree algorithm and K‑nearest neighbor in predicting stroke was 95.42% and 94.18%, respectively. Conclusions: The two algorithms, C4.5 decision tree algorithm and K‑nearest neighbor, can be used in order to predict stroke in high risk groups.
Keywords
Data mining , decision tree , K‑nearest neighbor , prediction , stroke
Journal title
International Journal of Preventive Medicine (IJPM)
Journal title
International Journal of Preventive Medicine (IJPM)
Record number
2566831
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