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Breast Cancer Analysis and Prognosis Using Machine Learning

Shetty, Simitha (2020) Breast Cancer Analysis and Prognosis Using Machine Learning. Masters thesis, Dublin, National College of Ireland.

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With increasing number of cases and deaths every year, breast cancer is one of the most common health problem in today's society and prime cause of death in females. This research focuses on predicting breast cancer severity i.e. "benign" or "malignant" at an earlier phase using Machine Learning algorithms so that appropriate treatment can be provided to reduce number of fatalities in women. This study proposes classification models that will help to identify the severity of dis- ease with the use of two datasets containing breast mass and breast tissue samples. The research aims at balancing the dataset first by applying Synthetic Minority Over-Sampling Technique and then compares seven models, Support Vector Machine, Naive Bayes, Logistic Regression, K-Nearest Neighbour, Classification and Regression Tree, Artificial Neural Network and Extreme Gradient Boosting. As the study focuses on the sensitivity of each model i.e. the True Positive Rate, the output shows that in the first dataset, K-Nearest Neighbour performed best with sensitivity of 97.10% whereas in the second dataset, Extreme Gradient Boosting performed better with 97.81% sensitivity. However on analyzing Figure 10, it can be seen that Artificial Neural Network and K-Nearest Neighbour performed good on both the datasets and these models can be used in predicting the breast cancer severity.

Item Type: Thesis (Masters)
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
Q Science > QA Mathematics > Computer software
T Technology > T Technology (General) > Information Technology > Computer software
R Medicine > R Medicine (General)
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: Dan English
Date Deposited: 15 Jun 2020 11:58
Last Modified: 15 Jun 2020 11:58

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