Title: A comparative analysis of classifiers in cancer prediction using multiple data mining techniques
Authors: Seyed Mohammad Jafar Jalali; Sérgio Moro; Mohammad Reza Mahmoudi; Keramat Allah Ghaffary; Mohsen Maleki; Aref Alidoostan
Addresses: Department of Information Technology, Allameh Tabataba'i University, Tehran, Iran ' Instituto Universitário de Lisboa (ISCTE-IUL), ISTAR-IUL, Lisboa, Portugal; ALGORITMI Research Centre, University of Minho, Guimarães, Portugal ' Department of Statistics, Fasa University, Fasa, Iran ' Department of Statistics, Fasa University, Fasa, Iran ' Department of Statistics, Shiraz University, Shiraz, Iran ' Department of Information Technology, Islamic Azad University of Science and Research of Fars, Shiraz, Iran
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 the 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.
Keywords: cancer prediction; data mining; classifiers; association rules.
International Journal of Business Intelligence and Systems Engineering, 2017 Vol.1 No.2, pp.166 - 178
Received: 29 Dec 2016
Accepted: 12 Jun 2017
Published online: 11 Dec 2017 *