Breast Cancer Prediction Model Using Machine Learning - Muhammad Amin, Bakri; Inna, Ekawati (INTI International University, 2021)
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%).