Class
Article
College
Jon M. Huntsman School of Business
Department
Management Information Systems Department
Faculty Mentor
Zsolt Ugray
Presentation Type
Poster Presentation
Abstract
This study conducts a comparative analysis of feature selection techniques and machine learning models to improve breast cancer risk prediction using the Wisconsin Breast Cancer Dataset. Through evaluating univariate feature selection, recursive feature elimination, and principal component analysis, alongside ten machine learning models, the research aims to identify the most effective combination for accurate diagnosis.
Performance metrics such as accuracy, precision, recall, and F1 score guide the analysis.
The findings suggest that integrating specific feature selection methods with particular machine learning algorithms significantly enhances predictive accuracy, offering insights for developing more effective breast cancer detection tools and improving patient outcomes.
Location
Logan, UT
Start Date
4-9-2024 10:30 AM
End Date
4-9-2024 11:20 AM
Included in
A Comprehensive Study: Comparing Machine Learning Techniques for Breast Cancer Prediction
Logan, UT
This study conducts a comparative analysis of feature selection techniques and machine learning models to improve breast cancer risk prediction using the Wisconsin Breast Cancer Dataset. Through evaluating univariate feature selection, recursive feature elimination, and principal component analysis, alongside ten machine learning models, the research aims to identify the most effective combination for accurate diagnosis.
Performance metrics such as accuracy, precision, recall, and F1 score guide the analysis.
The findings suggest that integrating specific feature selection methods with particular machine learning algorithms significantly enhances predictive accuracy, offering insights for developing more effective breast cancer detection tools and improving patient outcomes.