Class
Article
College
Jon M. Huntsman School of Business
Department
Management Information Systems Department
Faculty Mentor
Zsolt Ugray
Presentation Type
Poster Presentation
Abstract
The comparative analysis of feature selection and machine learning models for breast cancer risk prediction aims to develop accurate and efficient models for diagnosing breast cancer. In this analysis, we explore different feature selection techniques and machine learning models to identify the most effective feature combination for breast cancer risk prediction. Our study provides valuable insights into the importance of feature selection and model selection in developing accurate breast cancer risk prediction models. Using the three features provide high accuracy to detect breast cancer. Overall, this study highlights the importance of combining feature selection techniques with machine learning algorithms to develop accurate and efficient models for breast cancer risk prediction.The findings of this study are a very simple method to diagnose to improve breast cancer, ultimately leading to better outcomes for patients.
Location
Logan, UT
Start Date
4-12-2023 1:30 PM
End Date
4-12-2023 2:30 PM
Included in
Comparative Analysis of Feature Selection and Machine Learning Models for Breast Cancer Risk Prediction
Logan, UT
The comparative analysis of feature selection and machine learning models for breast cancer risk prediction aims to develop accurate and efficient models for diagnosing breast cancer. In this analysis, we explore different feature selection techniques and machine learning models to identify the most effective feature combination for breast cancer risk prediction. Our study provides valuable insights into the importance of feature selection and model selection in developing accurate breast cancer risk prediction models. Using the three features provide high accuracy to detect breast cancer. Overall, this study highlights the importance of combining feature selection techniques with machine learning algorithms to develop accurate and efficient models for breast cancer risk prediction.The findings of this study are a very simple method to diagnose to improve breast cancer, ultimately leading to better outcomes for patients.