Cervical cancer is one type of gynaecological cancers and the majority of these complications of cervical cancer are associated to human papillomavirus infection. There are numerous risk factors associated with cervical cancer. It is important to recognize the significance of test variables of cervical cancer for categorizing the patients based on the results. This work intended to attain deeper understanding by applying machine learning techniques in R to analyze the risk factors of cervical cancer. Various types of feature selection techniques are explored in this work to determine about important attributes for cervical cancer prediction. Significant features are identified over various iterations of model training through several feature selection methods and an optimized feature selection model has been formed. In addition, this work aimed to build few classifier models using C5.0, random forest, rpart, KNN and SVM algorithms. Maximum possibilities were explored for training and performance evaluation of all the models. The performance and prediction exactness of these algorithms are conferred in this paper based on the outcomes attained. Overall, C5.0 and random forest classifiers have performed reasonably well with comprehensive accuracy for identifying women exhibiting clinical sign of cervical cancer.
Over the last decade (or two) the pendulum of developer mindshare has swung decidedly towards agile software development from a more traditional engineering approach to software development. To ascertain the essential differences and any possible incompatibilities between these two software development paradigms this research investigates a number of traditional and agile methodologies, methods, and techniques. The essential differences between traditional software engineering and agile software development are found not to be (as one may first suspect from a cursory consideration) related to iteration length or project management, but rather more related to other attributes like the variety of models employed, the purpose of the models, and the approach to modeling. In the end though the two approaches are not seen to be incompatible, leading to the future possibility of an Agile Software Engineering (ASE).
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