Comparative study of Multi-Label Classification, Ensemble Based Learning and Artificial Neural Network for Cervical Cancer Prediction
Used medical dataset (from Kaggle / UCI ML Repository) with 32 features & 4 labels of patients for predicting cervical cancer.
- Compared performance of the three methods consisting of the ML models – Naïve Bayes, Support Vector Machine (SVM), Logistic Regression, K-Nearest Neighbor (KNN) and Decision Tree.
- Stacking classifier of Ensemble based learning gave the best accuracy of 98.02% for classifying the target variables.