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Optimized Model for Cervical Cancer Detection Using Binary Cuckoo Search

机译：使用二进制杜鹃搜索宫颈癌检测的优化模型

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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 alot of time and computational resources. Hence, reducing the features and taking only essential featuresinto consideration is an effective solution.Objective: The aim of the research is to identify the relevant features in the classification of cancerand optimize the model. Feature selection increases the accuracy percentage of any classifier. Thebinary cuckoo search optimization algorithm was applied to explore the important features in the attributelist.Method: In our research, the performance of the proposed framework has been verified via instigatingit with base classifiers such as Random Forest, kernel SVM, Decision Tree and kNN and thenevaluated the results with and without Binary Cuckoo Optimization (BCO). The proposed methodinvolves cuckoo search algorithm for selection of optimal feature split points. Cuckoo Search Optimizationis a nature stimulated and breeding process of the cuckoo bird’s algorithm to predict bestglobal solution.Results: The results produced only selected features vital for prediction of cancer. In addition, itsperformance 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 classifierswith an accuracy of 94.7 % increased to 97% after Cuckoo Optimization.
机译：背景：宫颈癌是印度女性中妇女死亡原因之一。癌症的认可和早期发现似乎是诊断过程中的富有成效的方法。它将成为医疗行业的福音。使用所有功能预测宫颈癌需要很多时间和计算资源。因此，减少特征和仅仅是基本特征Into考虑是一个有​​效的解决方案。目的：研究的目的是识别癌症分类中的相关特征，优化模型。特征选择会增加任何分类器的精度百分比。应用了杜鹃搜索优化算法来探讨attributelist.method中的重要功能具有和没有二进制Cuckoo优化（BCO）的结果。所提出的方法探测器杜鹃搜索算法，用于选择最佳特征分裂点。 Cuckoo搜索优化杜鹃鸟算法的自然刺激和繁殖过程，以预测最佳策略。结果：结果仅产生了对癌症预测至关重要的选择。此外，ITSPERFANCES与其他因素相似，例如精度，精度，召回和特异性，以及F测量值。结论：实验结果表明，决策树分类器优于所有其他分类器，在杜鹃岛后的准确度为94.7％增加到97％优化。

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