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    •   BracU IR
    • School of Data and Sciences (SDS)
    • Department of Computer Science and Engineering (CSE)
    • Thesis & Report, BSc (Computer Science and Engineering)
    • View Item
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    Machine learning as an indicator for breast cancer prediction

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    14101060,14101143,14101146_CSE.pdf (1.043Mb)
    Date
    2018-12
    Publisher
    BRAC University
    Author
    Shadman, Tahsin Mohammed
    Akash, Fahim Shahriar
    Ahmed, Mayaz
    Metadata
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    URI
    http://hdl.handle.net/10361/11431
    Abstract
    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
    Keywords
    Breast cancer; Cancer prediction; Machine learning
     
    LC Subject Headings
    Machine learning
     
    Description
    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.
    Department
    Department of Computer Science and Engineering, BRAC University
    Collections
    • Thesis & Report, BSc (Computer Science and Engineering)

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