NGBFA Feature Selection Algorithm-based Hybrid Ensemble Classifier to Predict Cervical Cancer
Keywords:
feature selection, Machine learning, cervical cancer, Hybrid Ensemble, classificationAbstract
Early diagnosis may cure cervical cancer. Researchers have struggled to prediction the disease's course because there are no early indications. Several machine learning methods have predicted CC in the past decade. Ensemble techniques generate and integrate several models for more accurate results. This contrasts with single-classifier prediction. During this research, we established "Robust Model Stacking: A Hybrid Ensemble." This classifier runs a homogeneous classifier-based classifications at the base level, then a heterogeneous ensemble that predicts additional data using majority voting (soft). This study included 858 patients, 32 risk indicator characteristics, and four CC diagnosis test targets. SMOTE oversampling solved the data imbalance problem. For each of the dataset's four goal variables, accuracy, recall, f1-score, precision, and AUC-ROC were used to assess the model. The proposed biopsy approach is 98% accurate, Hinselmann 97%, Schiller 96.09%, and Citology 93%. Ensemble learning improves prediction accuracy and reduces bias and variation in this study.
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