Feature Selection and Biomarker Identification in Ovarian Cancer Clinical Datasets Using Supervised Algorithms - Rehman, Rizwan; Konwar, Pinakshi (Communications in Mathematics and Applications, 2025)
Ovarian Cancer is one of the most common diseases in females. It is about the unusual growth of cancer cells in the ovaries. Even with improvements in medical research and treatment, it still plays a big role in deaths caused by cancer. Early prediction and detection of this disease may save many lives. In this study, three datasets have been considered to find the features which might be useful in the prediction of this disease. In this study, an ensemble of Random Forest Classifier, XGBoost and Mutual Information Gain was applied and then voting technique was used to find the most important features. The important features from each dataset were compared with each other to get the resultant features MAF, PAX8, SERINC1, SFN, SPON1, CREBL2, ST13, and INTS5. Four machine learning techniques were used namely Random Forest, eXtreme Gradient Boosting, Light Gradient Boosting and ANN to train and evaluate using Accuracy, Precison, Recall and F1 Score. The cross-validation across independent datasets enhances the generalizability of the identified biomarkers.