Breast cancer prediction using different machine learning models
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2019.
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Brac University
2020
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10361-137802022-01-26T10:15:46Z Breast cancer prediction using different machine learning models Khandker Al- Muhaimin Tahsan Mahmud Sudeepta Acharya Ashiqul Islam Alam, Md. Ashraful Department of Computer Science and Engineering, Brac University Supervised learning Comparative study Breast cancer Cancer prediction Adaboost classifier PCA Image processing Machine learning 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 32-33). Breast cancer is often the most lethal diseases with a large mortality rate especially among women. Despite the severe effect of the disease, it is possible to pinpoint the genre of breast cancer using diff t machine learning algorithms. However, many of these algorithms perform differenttly depending on their types and complexities. In our work, we have analyzed and compared the classification results of various ma- chine learning models and fi out the best model to classify between diff t types of breast cancers. We have used Logistic Regression, SVM, Random Forest, AdaBoost Tree, NaA˜ ve Bayes, K neighbor classifier, Decision Tree and Gaussian Process classifiers for our comparative study. Additionally, we applied dimensional- ity reduction in order to simplify our dataset from 30 features to 2 features so that the computation time can be reduced. Our task is to critically analysis different data and to classify them with respect to the efficacy of each algorithm in terms of accuracy, precision, recall and F1 Score. Without dimensionality reduction, our best accuracy was 97.36 percent which was found using SVM. Then again, with dimensionality reduction, the prime accurate result was 98.24 percent which was achieved by SVM and the computation time also decreased. Khandker Al- Muhaimin Tahsan Mahmud Sudeepta Acharya Ashiqul Islam B. Computer Science 2020-02-18T06:11:12Z 2020-02-18T06:11:12Z 2019 2019-08 Thesis ID 14101022 ID 14101224 ID 14101032 ID 13301010 http://hdl.handle.net/10361/13780 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. 33 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Supervised learning Comparative study Breast cancer Cancer prediction Adaboost classifier PCA Image processing Machine learning |
| spellingShingle |
Supervised learning Comparative study Breast cancer Cancer prediction Adaboost classifier PCA Image processing Machine learning Khandker Al- Muhaimin Tahsan Mahmud Sudeepta Acharya Ashiqul Islam Breast cancer prediction using different machine learning models |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2019. |
| author2 |
Alam, Md. Ashraful |
| author_facet |
Alam, Md. Ashraful Khandker Al- Muhaimin Tahsan Mahmud Sudeepta Acharya Ashiqul Islam |
| format |
Thesis |
| author |
Khandker Al- Muhaimin Tahsan Mahmud Sudeepta Acharya Ashiqul Islam |
| author_sort |
Khandker Al- Muhaimin |
| title |
Breast cancer prediction using different machine learning models |
| title_short |
Breast cancer prediction using different machine learning models |
| title_full |
Breast cancer prediction using different machine learning models |
| title_fullStr |
Breast cancer prediction using different machine learning models |
| title_full_unstemmed |
Breast cancer prediction using different machine learning models |
| title_sort |
breast cancer prediction using different machine learning models |
| publisher |
Brac University |
| publishDate |
2020 |
| url |
http://hdl.handle.net/10361/13780 |
| work_keys_str_mv |
AT khandkeralmuhaimin breastcancerpredictionusingdifferentmachinelearningmodels AT tahsanmahmud breastcancerpredictionusingdifferentmachinelearningmodels AT sudeeptaacharya breastcancerpredictionusingdifferentmachinelearningmodels AT ashiqulislam breastcancerpredictionusingdifferentmachinelearningmodels |
| _version_ |
1814308226313748480 |