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Prediction and Control of Stroke by Data Mining



Amini L1 ; Azarpazhouh R2 ; Farzadfar MT2 ; Mousavi SA3 ; Jazaieri F4 ; Khorvash F3 ; Norouzi R3 ; Toghianifar N3
Authors
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Authors Affiliations
  1. 1. Department of Computer Engineering and Information Technology, Payam Noor University, Tehran, Iran
  2. 2. Department of Neurology, Medical University of Mashhad Sciences, Mashhad, Iran
  3. 3. Department of Neurology, Isfahan Neurosciences Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
  4. 4. Department of Pharmacology, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran

Source: International Journal of Preventive Medicine Published:2013

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. © 2013, Isfahan University of Medical Sciences(IUMS). All rights reserved.
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