A hybrid stacking classifier with feature selection for handling imbalanced data

Abstract

Nowadays, cancer has become more alarming. This paper discusses the most significant Ovarian Cancer, Epithelial Ovarian Cancer (EOC), due to the low survival rate. The proposed algorithm for this work is a ‘Multi classifier ShapRFECV based EOC’ (MSRFECV-EOC) subtype analysis technique that utilized the EOC data from the National Centre for Biotechnology Information and Cancer Cell Line Encyclopedia websites for early identification of EOC using Machine Learning Techniques. This approach increases the data size, balances different classes of the data, and cuts down the enormous number of features unrelated to the disease of interest to prevent overfitting. To incorporate these functionalities, in the data preprocessing stage, OC-related gene names were taken from the Cancermine database and other OC-related works. Moreover, OC datasets were merged based on OC genes, and missing values of EOC subtypes were identified and imputed using Iterative Logistic Imputation. Synthetic Minority Oversampling Technique with an Edited Nearest Neighbors approach is applied to the imputed dataset. Next, in the Feature Selection phase, the most significant features for subtypes of EOC were identified by applying the Shapley Additive Explanations based on the Recursive Feature Elimination Cross-Validation (ShapRFECV) algorithm, preserving predefined features while selecting new EOC features. Eventually, an accuracy of 97% was achieved with Optuna-optimized Random Forest, which outperformed the existing models. SHAP plotted the most prominent features behind the classification. The Pickle tool saves much training time by preserving hidden parameter values of the model. In the final phase, by using the Stratified K Fold Stacking Classifier, the accuracy was improved to 98.9%.