An Evaluation Study on the Predictive Models of Breast Cancer Risk Factor Classification


Yee, Wen San and Ng, Hu and Yap, Timothy Tzen Vun and Goh, Vik Tor and Ng, Keng Hoong and Cher, Dong Theng (2022) An Evaluation Study on the Predictive Models of Breast Cancer Risk Factor Classification. Journal of Logistics, Informatics and Service Science, 9 (3). pp. 129-145. ISSN 2409-2665

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This research is intended to explore and evaluate various predictive models for the classification performance of breast cancer risk factors. First, data acquisition is being carried out to obtained three datasets from Breast Cancer Surveillance Consortium (BCSC). After that, data integration is performed to combine the datasets into one. Then, data preprocessing is performed to do data cleaning. Feature selection is executed to eliminate unrelated attributes. Data resampling is applied to resolve imbalanced data. Four classifiers namely Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), and Multilayer Perceptron (MLP) are used in classifying the risk factors of breast cancer. These four classifiers undergo training and testing data with 80-20, 70-30, and 60-40 train test splits. RF performs the best performance with 82% of accuracy at 80-20 train test split.

Item Type: Article
Uncontrolled Keywords: support vector machine, breast cancer, boruta feature selection, data resampling, logistic regression, random forest, multilayer perceptron
Subjects: Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions: Faculty of Computing and Informatics (FCI)
Faculty of Engineering (FOE)
Depositing User: Ms Nurul Iqtiani Ahmad
Date Deposited: 31 Oct 2022 02:39
Last Modified: 31 Oct 2022 02:39


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