Abstract
Background: Cervical Cancer is one of the leading causes of deaths among women in India. Accurate and early detection of cancer seems to be a fruitful approach in the diagnosis process. It will be a boon for the medical industry. Prediction of cervical cancer using all the features takes a lot of time and computational resources. Hence, reducing the features and taking only essential features into consideration is an effective solution.
Objective: The aim of the research is to identify the relevant features in the classification of cancer and optimize the model. Feature selection increases the accuracy percentage of any classifier. The binary cuckoo search optimization algorithm was applied to explore the important features in the attribute list.
Methods: In our research, the performance of the proposed framework has been verified via instigating it with base classifiers such as Random Forest, kernel SVM, Decision Tree and kNN and then evaluated the results with and without Binary Cuckoo Optimization (BCO). The proposed method involves cuckoo search algorithm for selection of optimal feature split points. Cuckoo Search Optimization is a nature stimulated and breeding process of the cuckoo bird’s algorithm to predict best global solution.
Results: The results produced only selected features vital for prediction of cancer. In addition, its performance has been paralleled against other factors such as Accuracy, Precision, Recall and Specificity and F-measure.
Conclusion: The experimental results show that Decision Tree classifier outperforms all other classifiers with an accuracy of 94.7% increased to 97% after Cuckoo Optimization.
Keywords: Cervical cancer, cuckoo optimization algorithm, decision tree classifier, feature selection, kernel SVM, kNN, random forest.
Graphical Abstract
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