Evaluating Machine Learning Algorithms for Breast Cancer Detection: a Study on Accuracy and Predictive Performance
Year: 2024
Type: article
Abstract: This study evaluates several machine learning algorithms—Support Vector Machine (SVM), Random Forest, Logistic Regression, Decision Tree (C4.5), and k-Nearest Neighbors (KNN)—for breast cancer detection using the Breast Cancer Wisconsin Diagnostic dataset. We implemented comprehensive pre-processing and model evaluation with Scikit-learn in Python. Our findings show that SVM achieved the highest accuracy, with 99.9% on the training set and 98.50% on the testing set, indicating superior performance in handling high-dimensional data. Random Forest also performed well, with accuracies of 98.5% and 98.20%, respectively. Logistic Regression and Decision Tree models provided reliable predictions when tuned, while KNN was less effective. SVM and Random Forest are recommended for clinical decision support systems due to their high accuracy and robustness.
Institutions: Trine University, Gannon University
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W4402413069