Comparative Analysis of Machine Learning Algorithms for Breast Cancer Prediction Using Fine Needle Aspirate Image Features

Breast cancer continues to be a significant cause of mortality among women worldwide, underscoring the importance of accurate and early detection to enhance survival rates. This research explores the effectiveness of several supervised machine learning models in predicting breast cancer using a specialized dataset. Five algorithms were evaluated: Logistic Regression, Decision Tree, Random Forest, Support Vector Machine (SVM), and Gradient Boosting. The dataset includes 569 samples with 30 continuous features derived from digitized images of fine needle aspiration (FNA) of breast tumors. The models were trained and assessed based on key metrics such as accuracy, precision, recall, and F1-score. Among the models, Random Forest and SVM delivered the best results, both achieving a 97.20% accuracy, which highlights the strength of ensemble and kernel-based methods in this application. Logistic Regression also performed commendably, reaching an accuracy of 95.10%, indicating that the feature space exhibits a high degree of linear separability. While Decision Tree and Gradient Boosting models demonstrated good performance, their results were slightly lower in comparison. These findings emphasize the capability of machine learning, particularly Random Forest and SVM, in supporting breast cancer diagnosis. Furthermore, the consistently high accuracy across models reflects the robustness of the features derived from FNA images for breast cancer classification.