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Title: A comparative analysis of classifiers in cancer prediction using multiple data mining techniques
Authors: Jalali, S. M.
Moro, S.
Mahmoudi, M. R.
Ghaffary, K. A.
Maleki, M.
Alidoostan, A.
Keywords: Cancer prediction
Data mining
Association rules
Issue Date: 2017
Publisher: Inderscience
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.
Peer reviewed: yes
DOI: 10.1504/IJBISE.2017.10009655
ISSN: 2051-5847
Appears in Collections:ISTAR-RI - Artigos em revistas científicas internacionais com arbitragem científica

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