Breast Cancer Detection Using Classifiers

Karim, Md. Rezaul; Habib, M. Musab; Zaman, Rahat

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dc.contributor.author	Karim, Md. Rezaul
dc.contributor.author	Habib, M. Musab
dc.contributor.author	Zaman, Rahat
dc.date.accessioned	2021-10-01T04:26:23Z
dc.date.available	2021-10-01T04:26:23Z
dc.date.issued	2014-11-15
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dc.identifier.uri	http://hdl.handle.net/123456789/1044
dc.description	Supervised by Prof. Dr. Mohammad Rakibul Islam, Department of Electrical and Electronic Engineering (EEE), Islamic University of Technology (IUT), Board Bazar, Gazipur-1704, Bangladesh.	en_US
dc.description.abstract	Detection of breast cancer is the major phase in Cancer Diagnosis. So, classifiers with higher accuracy are always superior. A classifier already carrying high accuracy and then leading it to higher accuracy offers very less chance to a patient to be wrongly classified. This book involves this kind of classifiers i.e. Naïve Bayes, J48 algorithms along with their performance evaluating criteria. To check up, java based WEKA classification is done with similar dataset & similar feature selection. Modification in typical Naïve Bayes introducing Multivariate Gaussian distribution results in higher accuracy in the thesis work. Fusion in predicted results of the Naïve Bayes & J48 introduces a new algorithm to detect both classifiers’ wrong predictions. So, counting a patient cancerous only in the case of two classifiers saying a patient cancerous leads to poor accuracy overall but more precise prediction. Our thesis work proposed for the last two algorithms while leaving a good overview of breast cancer detection through the Machine Learning Classifiers.	en_US
dc.language.iso	en	en_US
dc.publisher	Department of Electrical and Electronic Engineering, Islamic University of Technology (IUT), Board Bazar, Gazipur-1704, Bangladesh	en_US
dc.subject	Breast cancer detection, Machine learning classifier, Naïve Bayes, Decision Tree, J48, WEKA, Accuracy Greedy Algorithm, Identification Greedy Algorithm, Fusion.	en_US
dc.title	Breast Cancer Detection Using Classifiers	en_US
dc.type	Thesis	en_US