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A Machine Learning Approach to Prevent Cancer
Abstract
One of the most talked about diseases of the 21st century is none other than cancer. In this chapter, the authors take a closer look to prevent cancer through machine learning approach. At first, they ran their classifier models (e.g., decision tree, K-mean, SVM, etc.) to check which algorithm gives the best result in terms of choosing right features for further treatment. The classified results are compared, and then various feature reduction algorithm is being used to identify exactly which features affects the most. Various data mining algorithms are being used, namely rough-based theory, graph-based clustering, to extract the most important features which influence the results. In the next section they take a look in the cancer analytics part. A simulation model has been designed that can easily manage the patient flow in OPDs and a bed rotation model also have been designed to give patients an insight that how much time they will spend in the queue. Further they analyzed a risk analysis model for chemotherapy treatment, and finally, an econometric discussion has been drawn in how it affects the treatment.
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