A Machine Learning Approach for Early Detection of Breast Cancer: Performance Evaluation and Analysis
Tanusree Saha1, Sarmistha Santra2
1Tanusree Saha, Department of Information Technology, JIS College of Engineering, Kalyani (West Bengal), India.
2Sarmistha Santra, Student, Department of Computer Applications, JIS College of Engineering, Kalyani (West Bengal), India.
Manuscript received on 28 April 2025 | First Revised Manuscript received on 21 May 2025 | Second Revised Manuscript received on 18 October 2025 | Manuscript Accepted on 15 November 2025 | Manuscript published on 30 November 2025 | PP: 34-38 | Volume-6 Issue-1 November 2025 | Retrieval Number: 100.1/ijpmh.D108905040525 | DOI: 10.54105/ijpmh.D1089.06011125
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© The Authors. Published by Lattice Science Publication (LSP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Breast cancer is one of the most prevalent cancers affecting women globally and continues to be a leading cause of cancer-related deaths. Early and accurate diagnosis significantly improves survival rates, but conventional diagnostic techniques are often time-consuming, costly, and prone to subjective interpretation. To address these challenges, this study focuses on developing an efficient breast cancer detection system using machine learning (ML) and deep learning (DL) algorithms. By leveraging Convolutional Neural Networks (CNNs) and several traditional ML models, the system aims to classify breast cancer efficiently based on imaging data. Utilising the Wisconsin Breast Cancer Dataset (WBCD), the project evaluates the performance of various machine learning models, including SVM, KNN, Logistic Regression, Random Forest, Decision Tree, Naïve Bayes, AdaBoost, and XGBoost, based on accuracy, precision, and robustness. The outcomes indicate that machine learning can significantly enhance early detection efforts, providing clinicians with reliable decision-support tools.
Keywords: Breast Cancer, Machine Learning, Deep Learning, CNN, SVM, KNN, Logistic Regression, Random Forest, Decision Tree, Naïve Bayes, AdaBoost, Boost
Scope of the Article: Breastfeeding