Prediction of Breast Cancer Using Machine Learning Techniques

• Author(s): Abdulmalik Abdullahi; Yusuf Magaji Mada
• Paper ID: 1712217
• Page: 2154-2159
• Published Date: 28-11-2025
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 9 Issue 5 November-2025

Abstract

Breast cancer is a highly dangerous disease affecting women worldwide. Its prevalence is rapidly increasing, emphasizing the importance of early detection and preventive measures. This study focuses on utilizing various machine learning classification algorithms, including Support Vector Machine (SVM), Logistic Regression (LR), K Nearest Neighbor (KNN), Random Forest Classifier (RFC), and Decision Tree Classifier, to predict breast cancer in women. We will assess and compare these classifiers using metrics like accuracy, precision, recall, and f1-Score. We will utilize the breast cancer dataset from the UCI Machine Learning Repository, which is accessible to the public. The data will be split, with 80% allocated for training and 20% for testing purposes. Based on the outcomes, it is evident that the Random Forest Classifier outperformed the other classifiers across all evaluation criteria.

Keywords

Machine Learning, Breast Cancer, Classification, Accuracy, and Precision

Citations

IRE Journals:
Abdulmalik Abdullahi, Yusuf Magaji Mada "Prediction of Breast Cancer Using Machine Learning Techniques" Iconic Research And Engineering Journals Volume 9 Issue 5 2025 Page 2154-2159 https://doi.org/10.64388/IREV9I5-1712217

IEEE:
Abdulmalik Abdullahi, Yusuf Magaji Mada "Prediction of Breast Cancer Using Machine Learning Techniques" Iconic Research And Engineering Journals, 9(5) https://doi.org/10.64388/IREV9I5-1712217