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Jalali, S. M., Moro, S., Mahmoudi, M. R., Ghaffary, K. A., Maleki, M. & Alidoostan, A. (2017). A comparative analysis of classifiers in cancer prediction using multiple data mining techniques. International Journal of Business Intelligence and Systems Engineering. 1 (2), 166-178

S. M. Jalali et al.,  "A comparative analysis of classifiers in cancer prediction using multiple data mining techniques", in Int. Journal of Business Intelligence and Systems Engineering, vol. 1, no. 2, pp. 166-178, 2017

@article{jalali2017_1715073309205,
	author = "Jalali, S. M. and Moro, S. and Mahmoudi, M. R. and Ghaffary, K. A. and Maleki, M. and Alidoostan, A.",
	title = "A comparative analysis of classifiers in cancer prediction using multiple data mining techniques",
	journal = "International Journal of Business Intelligence and Systems Engineering",
	year = "2017",
	volume = "1",
	number = "2",
	doi = "10.1504/IJBISE.2017.10009655",
	pages = "166-178",
	url = "http://www.inderscience.com/jhome.php?jcode=ijbise"
}

TY  - JOUR
TI  - A comparative analysis of classifiers in cancer prediction using multiple data mining techniques
T2  - International Journal of Business Intelligence and Systems Engineering
VL  - 1
IS  - 2
AU  - Jalali, S. M.
AU  - Moro, S.
AU  - Mahmoudi, M. R.
AU  - Ghaffary, K. A.
AU  - Maleki, M.
AU  - Alidoostan, A.
PY  - 2017
SP  - 166-178
SN  - 2051-5847
DO  - 10.1504/IJBISE.2017.10009655
UR  - http://www.inderscience.com/jhome.php?jcode=ijbise
AB  - 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.
ER  -