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Hassanien, A. E., and J. M. Ali, "Rough set approach for classification of breast cancer mammogram images", International Workshop on Fuzzy Logic and Applications: Springer Berlin Heidelberg, pp. 224–231, 2003. Abstract

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Hassanien, A. E., and J. M. Ali, "Rough set approach for classification of breast cancer mammogram images", International Workshop on Fuzzy Logic and Applications: Springer Berlin Heidelberg, pp. 224–231, 2003. Abstract
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Hassanien, A. E., and J. M. H. Ali, "Rough set approach for generation of classification rules of breast cancer data", Informatica, vol. 15, no. 1: Institute of Mathematics and Informatics, pp. 23–38, 2004. Abstract
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Hassanien, A. E., and J. M. H. Ali, "Rough set approach for generation of classification rules of breast cancer data", Informatica, vol. 15, no. 1: Institute of Mathematics and Informatics, pp. 23–38, 2004. Abstract
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Ali, J., and A. E. Hassanien, "Rough Set Approach for Generation of Classification Rules of Breast Cancer Data.", Informatica, vol. 15, issue 1, pp. 23-38, 2004. Abstract

Extensive amounts of knowledge and data stored in medical databases require the development of specialized tools for storing, accessing, analysis, and effectiveness usage of stored knowledge and data. Intelligent methods such as neural networks, fuzzy sets, decision trees, and expert systems are, slowly but steadily, applied in the medical fields. Recently, rough set theory is a new intelligent technique was used for the discovery of data dependencies, data reduction, approximate set classification, and rule induction from databases.

In this paper, we present a rough set method for generating classification rules from a set of observed 360 samples of the breast cancer data. The attributes are selected, normalized and then the rough set dependency rules are generated directly from the real value attribute vector. Then the rough set reduction technique is applied to find all reducts of the data which contains the minimal subset of attributes that are associated with a class label for classification. Experimental results from applying the rough set analysis to the set of data samples are given and evaluated. In addition, the generated rules are also compared to the well-known IDS classifier algorithm. The study showed that the theory of rough sets seems to be a useful tool for inductive learning and a valuable aid for building expert systems.

Banerjee, S., H. Al-Qaheri, E. - S. A. El-Dahshan, and A. E. Hassanien, "Rough set approach in ultrasound biomicroscopy glaucoma analysis", Advances in Computer Science and Information Technology: Springer Berlin Heidelberg, pp. 491–498, 2010. Abstract
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Banerjee, S., H. Al-Qaheri, E. - S. A. El-Dahshan, and A. E. Hassanien, "Rough set approach in ultrasound biomicroscopy glaucoma analysis", Advances in Computer Science and Information Technology: Springer Berlin Heidelberg, pp. 491–498, 2010. Abstract
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P. K. Nizar Banu, H. H. Inbarani, A. T. Azar, H. S. Own, and A. E. Hassanien, "Rough Set Based Feature Selection for Egyptian Neonatal Jaundice ", The 2nd International Conference on Advanced Machine Learning Technologies and Applications , Egypt, November 17-19, , 2014.
Banu, P. K. N., H. H. Inbarani, A. T. Azar, H. S. Own, and A. E. Hassanien, "Rough set based feature selection for egyptian neonatal jaundice", International Conference on Advanced Machine Learning Technologies and Applications: Springer International Publishing, pp. 367–378, 2014. Abstract
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Al-Qaheri, H., S. Zamoon, A. E. Hassanien, and A. Abraham, "Rough set generating prediction rules for stock price movement", Computer Modeling and Simulation, 2008. EMS'08. Second UKSIM European Symposium on: IEEE, pp. 111–116, 2008. Abstract
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Al-Qaheri, H., S. Zamoon, A. E. Hassanien, and A. Abraham, "Rough set generating prediction rules for stock price movement", Computer Modeling and Simulation, 2008. EMS'08. Second UKSIM European Symposium on: IEEE, pp. 111–116, 2008. Abstract
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Salama, M. A., O. S. Soliman, I. Maglogiannis, A. E. Hassanien, and A. A. Fahmy, "Rough set-based identification of heart valve diseases using heart sounds", Rough Sets and Intelligent Systems-Professor Zdzisław Pawlak in Memoriam: Springer Berlin Heidelberg, pp. 475–491, 2013. Abstract
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Salama, M. A., O. S. Soliman, I. Maglogiannis, A. E. Hassanien, and A. A. Fahmy, "Rough set-based identification of heart valve diseases using heart sounds", Rough Sets and Intelligent Systems-Professor Zdzisław Pawlak in Memoriam: Springer Berlin Heidelberg, pp. 475–491, 2013. Abstract
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Hassanien, A. E., and J. M. H. Ali, "Rough sets analysis for reduct generation of breast cancer data", INTERNATIONAL JOURNAL OF COMPUTERS AND THEIR APPLICATIONS, vol. 10: ISCA, pp. 263–270, 2003. Abstract
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Elshazly, Hanaa, N.; Ghali, A. Korany, and A. E. Hassanien, "Rough sets and genetic algorithms: A hybrid approach to breast cancer classification", World Congress on Information and Communication Technologies (WICT), pp. 260 - 265 , India, Oct. 30 2012-Nov. Abstract

The use of computational intelligence systems such as rough sets, neural networks, fuzzy set, genetic algorithms, etc., for predictions and classification has been widely established. This paper presents a generic classification model based on a rough set approach and decision rules. To increase the efficiency of the classification process, boolean reasoning discretization algorithm is used to discretize the data sets. The approach is tested by a comparatif study of three different classifiers (decision rules, naive bayes and k-nearest neighbor) over three distinct discretization techniques (equal bigning, entropy and boolean reasoning). The rough set reduction technique is applied to find all the reducts of the data which contains the minimal subset of attributes that are associated with a class label for prediction. In this paper we adopt the genetic algorithms approach to reach reducts. Finally, decision rules were used as a classifier to evaluate the performance of the predicted reducts and classes. To evaluate the performance of our approach, we present tests on breast cancer data set. The experimental results obtained, show that the overall classification accuracy offered by the employed rough set approach and decision rules is high compared with other classification techniques including Bayes and k-nearest neighbor.

Elshazly, H. I., N. I. Ghali, A. M. E. Korany, and A. E. Hassanien, "Rough sets and genetic algorithms: A hybrid approach to breast cancer classification", Information and Communication Technologies (WICT), 2012 World Congress on: IEEE, pp. 260–265, 2012. Abstract
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Elshazly, H. I., N. I. Ghali, A. M. E. Korany, and A. E. Hassanien, "Rough sets and genetic algorithms: A hybrid approach to breast cancer classification", Information and Communication Technologies (WICT), 2012 World Congress on: IEEE, pp. 260–265, 2012. Abstract
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Elshazly, H. I., N. I. Ghali, A. M. E. Korany, and A. E. Hassanien, "Rough sets and genetic algorithms: A hybrid approach to breast cancer classification", Information and Communication Technologies (WICT), 2012 World Congress on: IEEE, pp. 260–265, 2012. Abstract
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Li, T., H. S. Nguyen, G. Wang, J. W. Grzymala-Busse, R. Janicki, A. - E. Hassanien, and H. Yu, Rough Sets and Knowledge Technology: 7th International Conference, RSKT 2012, Chengdu, China, August 17-20, 2012, Proceedings, : Springer, 2012. Abstract
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Li, T., H. S. Nguyen, G. Wang, J. W. Grzymala-Busse, R. Janicki, A. - E. Hassanien, and H. Yu, Rough Sets and Knowledge Technology: 7th International Conference, RSKT 2012, Chengdu, China, August 17-20, 2012, Proceedings, : Springer, 2012. Abstract
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Hassanien, A. E., A. Abraham, J. F. Peters, G. Schaefer, and C. Henry, "Rough sets and near sets in medical imaging: a review", IEEE Transactions on Information Technology in Biomedicine, vol. 13, no. 6: IEEE, pp. 955–968, 2009. Abstract
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Hassanien, A. E., M. E. Abdelhafez, and H. S. Own, "Rough Sets Data Analysis in Knowledge Discovery: A Case of Kuwaiti Diabetic Children Patients", Advances in Fuzzy Systems,, vol. 2008, issue 1, pp. 13, 2008. AbstractWebsite

The main goal of this study is to investigate the relationship between psychosocial variables and diabetic children patients and to obtain a classifier function with which it was possible to classify the patients on the basis of assessed adherence level. The rough set theory is used to identify the most important attributes and to induce decision rules from 302 samples of Kuwaiti diabetic children patients aged 7–13 years old. To increase the efficiency of the classification process, rough sets with Boolean reasoning discretization algorithm is introduced to discretize the data, then the rough set reduction technique is applied to find all reducts of the data which contains the minimal subset of attributes that are associated with a class label for classification. Finally, the rough sets dependency rules are generated directly from all generated reducts. Rough confusion matrix is used to evaluate the performance of the predicted reducts and classes. A comparison between the obtained results using rough sets with decision tree, neural networks, and statistical discriminate analysis classifier algorithms has been made. Rough sets show a higher overall accuracy rates and generate more compact rules.

Hassanien, A. E., M. E. Abdelhafez, and H. S. Own, "Rough sets data analysis in knowledge discovery: A case of kuwaiti diabetic children patients", Advances in fuzzy Systems, vol. 8: Hindawi Publishing Corp., pp. 2, 2008. Abstract
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Hassanien, A. E., M. E. Abdelhafez, and H. S. Own, "Rough sets data analysis in knowledge discovery: A case of kuwaiti diabetic children patients", Advances in fuzzy Systems, vol. 8: Hindawi Publishing Corp., pp. 2, 2008. Abstract
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Hassanien, A. E., and H. Own, "Rough sets for prostate patient analysis", Proceedings of International Conference on Modeling and Simulation (MS2006), 2006. Abstract
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