Machine learning as an indicator for breast cancer prediction

Includes bibliographical references (pages 53-54).

Sonraí bibleagrafaíochta
Príomhchruthaitheoirí: Shadman, Tahsin Mohammed, Akash, Fahim Shahriar, Ahmed, Mayaz
Rannpháirtithe: Alam, Md.Ashraful
Formáid: Tráchtas
Teanga:English
Foilsithe / Cruthaithe: BRAC University 2019
Ábhair:
Rochtain ar líne:http://hdl.handle.net/10361/11431
id 10361-11431
record_format dspace
spelling 10361-114312022-01-26T10:21:52Z Machine learning as an indicator for breast cancer prediction Shadman, Tahsin Mohammed Akash, Fahim Shahriar Ahmed, Mayaz Alam, Md.Ashraful Department of Computer Science and Engineering, BRAC University Breast cancer Cancer prediction Machine learning Machine learning Includes bibliographical references (pages 53-54). Cataloged from PDF version of thesis. This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018. Affecting roughly around 10 percent of the women across the globe in some stage of their lives,Breast Cancer has stood out to be one of the most feared and frequently occurring cancers at present among women[1]. While the cure for this cancer is now available in almost all first world and some of the third world nations,the main dilemma takes place when the cancer can not be correctly identified at the very initial stages. Machine Learning,in this field has proved to play a vital role in predicting diseases such as cancers alike.Classification and data mining methods so far have been reliant and an effective way to classify data.Especially in medical field,these methods have been used to predict and to make decisions.In this paper,we have successfully used six classification techniques in the form of Decision Tree, K-Neighbors, Linear Discriminant Analysis(LDA), Logistic Regression, Naïve Bayes and Support Vector Machine(SVM)on the Wiscons in Breast Cancer(original)data sets,both before and after applying Principal Component Analysis.The main objective is to assess the correctness in classifying data with respect to efficiency and effectiveness of each algorithm in terms of accuracy,precision,recall,specificity and F1 Score. Experimental results have shown that Logistic Regression(recal score=1.000)and Support Vector Analysis(recall score =1.000)with PCA performs better when it comes to Breast Cancer Prediction for his data set. Keywords:Classification;Decision tree;Machine learning;Support vector machine; Principal Component Analysis,Recall,10-Fold cross-validation Tahsin Mohamed Shadman Fahim Shahriar Akash Mayaz Ahmed B. Computer Science and Engineering 2019-02-18T05:36:46Z 2019-02-18T05:36:46Z 2018 2018-12 Thesis ID 14101060 ID 14101146 ID 14101143 http://hdl.handle.net/10361/11431 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. 54 pages application/pdf BRAC University
institution Brac University
collection Institutional Repository
language English
topic Breast cancer
Cancer prediction
Machine learning
Machine learning
spellingShingle Breast cancer
Cancer prediction
Machine learning
Machine learning
Shadman, Tahsin Mohammed
Akash, Fahim Shahriar
Ahmed, Mayaz
Machine learning as an indicator for breast cancer prediction
description Includes bibliographical references (pages 53-54).
author2 Alam, Md.Ashraful
author_facet Alam, Md.Ashraful
Shadman, Tahsin Mohammed
Akash, Fahim Shahriar
Ahmed, Mayaz
format Thesis
author Shadman, Tahsin Mohammed
Akash, Fahim Shahriar
Ahmed, Mayaz
author_sort Shadman, Tahsin Mohammed
title Machine learning as an indicator for breast cancer prediction
title_short Machine learning as an indicator for breast cancer prediction
title_full Machine learning as an indicator for breast cancer prediction
title_fullStr Machine learning as an indicator for breast cancer prediction
title_full_unstemmed Machine learning as an indicator for breast cancer prediction
title_sort machine learning as an indicator for breast cancer prediction
publisher BRAC University
publishDate 2019
url http://hdl.handle.net/10361/11431
work_keys_str_mv AT shadmantahsinmohammed machinelearningasanindicatorforbreastcancerprediction
AT akashfahimshahriar machinelearningasanindicatorforbreastcancerprediction
AT ahmedmayaz machinelearningasanindicatorforbreastcancerprediction
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