Best feature selection and data visualization for breast cancer prediction

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2019.

Sonraí bibleagrafaíochta
Príomhchruthaitheoirí:	Hemel, Tanjim Ahmed, Parvez, Rohan
Rannpháirtithe:	Mobin, Md. Iftekharul
Formáid:	Tráchtas
Teanga:	English
Foilsithe / Cruthaithe:	Brac University 2020
Ábhair:	Computer aided diagnosis Convolutional neural network Support vector machine Breast cancer detection Logistic regression Random forest K-Nearest neighbours Naive bayes PCA FNA Artifcial neural network
Rochtain ar líne:	http://hdl.handle.net/10361/13628

id	10361-13628
record_format	dspace
spelling	10361-136282022-01-26T10:20:05Z Best feature selection and data visualization for breast cancer prediction Hemel, Tanjim Ahmed Parvez, Rohan Mobin, Md. Iftekharul Department of Computer Science and Engineering, Brac University Computer aided diagnosis Convolutional neural network Support vector machine Breast cancer detection Logistic regression Random forest K-Nearest neighbours Naive bayes PCA FNA Artifcial neural network This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2019. Cataloged from PDF version of thesis. Includes bibliographical references (pages 26-27). Breast cancer (BC) is one of the most common cancers among women worldwide, representing the majority of new cancer cases and cancer-related deaths according to global statistics, making it a signifcant public health problem in today's society. In medical diagnosis, the forecast of an infection goes about as a signifcant center in breaking down the therapeutic pictures. The undesirable cell development in any piece of the organ is known as tumor. The tumor might be favorable or harmful. Threatening tumor is viewed as the most risky tissue. There are diferent specialists learned about the forecast of bosom malignancy. This paper aims to review on various data set techniques that are specifcally considered on breast cancer prediction and also to investigate which feature set is responsible for the disease and rapid growth of cancer cells as we are selecting the best features. From primarily given data set we can measure which parameter is responsible for cancer cells and which features can make the nearest perfect outcomes. It is presently possible to make precise Computer Aided Diagnosis (CAD)[7] framework so as to make the whole procedure of distinguishing a dangerous tumor more asset profcient and efcient through appropriate usage. This paper displays the relative investigation of various machine learning calculations and their outcomes in anticipating destructive tumors. For example, Decision Tree, Support Vector Machine, K-Nearest Neighbors, Linear Discriminant Analysis, Naive Bayes and Logistic Regression with and without PCA on a dataset with 30 highlights removed from a digitized picture of a Fine Needle Aspirate (FNA)[19] of a breast mass. Profound learning models like Artifcial Neural System and Convolutional Neural Network are utilized and their exhibitions are looked at. Tanjim Ahmed Hemel Rohan Parvez B. Computer Science 2020-01-20T04:00:42Z 2020-01-20T04:00:42Z 2019 2019-09 Thesis ID 14301013 ID 14101199 http://hdl.handle.net/10361/13628 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 27 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	Computer aided diagnosis Convolutional neural network Support vector machine Breast cancer detection Logistic regression Random forest K-Nearest neighbours Naive bayes PCA FNA Artifcial neural network
spellingShingle	Computer aided diagnosis Convolutional neural network Support vector machine Breast cancer detection Logistic regression Random forest K-Nearest neighbours Naive bayes PCA FNA Artifcial neural network Hemel, Tanjim Ahmed Parvez, Rohan Best feature selection and data visualization for breast cancer prediction
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2019.
author2	Mobin, Md. Iftekharul
author_facet	Mobin, Md. Iftekharul Hemel, Tanjim Ahmed Parvez, Rohan
format	Thesis
author	Hemel, Tanjim Ahmed Parvez, Rohan
author_sort	Hemel, Tanjim Ahmed
title	Best feature selection and data visualization for breast cancer prediction
title_short	Best feature selection and data visualization for breast cancer prediction
title_full	Best feature selection and data visualization for breast cancer prediction
title_fullStr	Best feature selection and data visualization for breast cancer prediction
title_full_unstemmed	Best feature selection and data visualization for breast cancer prediction
title_sort	best feature selection and data visualization for breast cancer prediction
publisher	Brac University
publishDate	2020
url	http://hdl.handle.net/10361/13628
work_keys_str_mv	AT hemeltanjimahmed bestfeatureselectionanddatavisualizationforbreastcancerprediction AT parvezrohan bestfeatureselectionanddatavisualizationforbreastcancerprediction
_version_	1814309197617037312

Best feature selection and data visualization for breast cancer prediction

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