Breast Cancer Prediction Model Using Machine Learning

Muhammad Amin, Bakri and Inna, Ekawati (2021) Breast Cancer Prediction Model Using Machine Learning. Journal of Data Science, 2021 (02). ISSN 2805-5160

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Breast cancer requires early detection, hence it can be prevented earlier or treated more optimally. This article aims to demonstrate predictive modelling of breast cancer and evaluate the accuracy of its predictions using a machine learning approach. This study uses secondary data from the Wisconsin Breast Cancer Dataset (BCWD) which consists of predictive factors for breast cancer and labels for benign or malignant cancers that result. Modelling with machine learning is done by selecting three candidate algorithms, namely Random Forest, Support Vector Machine, and Logistic Regression. Evaluation of the classification performance of each algorithm is carried out by analysing its sensitivity, specificity, and accuracy. The experimental results show that Random Forest has better prediction accuracy (99.6%) followed by Support Vector Machine (98.7%), and Logistic Regression (93.9%).

Item Type: Article
Uncontrolled Keywords: Prediction Model, Breast Cancer, Machine Learning Algorithm
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
Depositing User: Unnamed user with email
Date Deposited: 25 Aug 2021 06:29
Last Modified: 26 Aug 2021 10:26

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