. (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][167][168][169][170][171][172][173][174][175][176][177][178] Further information on publisher's website: 10.150410. /IJBISE.2017 Publisher's copyright statement: This is the peer reviewed version of the following article: 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, which has been published in final form at https://dx.doi.org/10.1504/IJBISE.2017.10009655. This article may be used for non-commercial purposes in accordance with the Publisher's Terms and Conditions for self-archiving. Use policyCreative Commons CC BY 4.0 The full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-profit purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in the Repository • the full-text is not changed in any wayThe full-text must not be sold in any format or medium without the formal permission of the copyright holders. AbstractIn 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 2 accuracy of 93%. It is worth mentioning that the approach proposed can be applied for detecting other diseases as well.
. (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][167][168][169][170][171][172][173][174][175][176][177][178] Further information on publisher's website: 10.150410. /IJBISE.2017 Publisher's copyright statement: This is the peer reviewed version of the following article: 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, which has been published in final form at https://dx.doi.org/10.1504/IJBISE.2017.10009655. This article may be used for non-commercial purposes in accordance with the Publisher's Terms and Conditions for self-archiving. Use policyCreative Commons CC BY 4.0 The full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-profit purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in the Repository • the full-text is not changed in any wayThe full-text must not be sold in any format or medium without the formal permission of the copyright holders. AbstractIn 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 2 accuracy of 93%. It is worth mentioning that the approach proposed can be applied for detecting other diseases as well.
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