Application of Machine Learning Techniques for Predicting Breast Cancer
DOI:
https://doi.org/10.24191/jcrinn.v11i2.564Keywords:
Prediction, Machine Learning, Random Forest, Logistic Regression, Decision Tree, Breast CancerAbstract
Breast cancer is the most prevalent invasive cancer in women and the second leading cause of cancer-related mortality among women. Interest in breast cancer research and prevention has surged recently. With the advent of data mining techniques, researchers can now efficiently extract valuable information from large databases, facilitating prediction, classification, and clustering. In this study, three classification models, namely Decision Tree, Random Forest, and Logistic Regression, were used to classify datasets related to breast cancer. The goal was to develop an accurate model to predict breast cancer and reduce the risk of death from the disease. The performance of these models was evaluated using three metrics: Precision, Recall, and F1 Score. Prediction accuracy was also measured. Comparative experiments in this study revealed that the Random Forest model outperformed the other two techniques in terms of performance and accuracy. Consequently, the study's model demonstrates significant clinical and referential value in real-world applications.
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Copyright (c) 2026 Wan Nasuha Amira Ayob, Nor Hayati Shafii, Nur Fatihah Fauzi, Diana Sirmayunie Mohd Nasir, Nor Azriani Mohamad Nor (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
