Prediction of Early Diagnosis in Ovarian Cancer Patients Using Machine Learning Approaches with Boruta and Advanced Feature Selection

This study highlights the importance of choosing appropriate machine learning algorithms and feature selection techniques for ovarian cancer diagnosis. Boruta and RFE showed high accuracy. By reducing the number of features from 50 to the most relevant ones, clinicians can make more precise diagnoses, enhance patient outcomes, and reduce unnecessary tests.