Scientific journal paper
A comparative analysis of classifiers in cancer prediction using multiple data mining techniques
Seyed Mohammad Jafar Jalali (Jalali, S. M.); Sérgio Moro (Moro, S.); Mohammad Reza Mahmoudi (Mahmoudi, M. R.); Keramat Allah Ghaffary (Ghaffary, K. A.); Mohsen Maleki (Maleki, M.); Aref Alidoostan (Alidoostan, A.);
Journal Title
International Journal of Business Intelligence and Systems Engineering
Year (definitive publication)
2017
Language
English
Country
Switzerland
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Times Cited: 33

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Abstract
In recent years, application of data mining methods in health industry has received increased attention from both health professionals and scholars. This paper presents a data mining framework for detecting breast cancer based on real data from one of Iran hospitals by applying association rules and the most commonly used classifiers. The former were adopted for reducing the size of datasets, while the latter were chosen for cancer prediction. A k-fold cross validation procedure was included for evaluating the performance of the proposed classifiers. Among the six classifiers used in this paper, support vector machine achieved the best results, with an accuracy of 93%. It is worth mentioning that the approach proposed can be applied for detecting other diseases as well.
Acknowledgements
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Keywords
Cancer prediction,Data mining,Classifiers,Association rules
Funding Records
Funding Reference Funding Entity
UID/MULTI/0446/2013 Fundação para a Ciência e a Tecnologia