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Machine Learning Based Approaches for Cancer Prediction: A Survey

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Document pages: 6 pages

Abstract: Cancer is a critical disease from many years. This leads to death if it is not diagnosed at early stage. Computer Science & Engineering is used in Bioinformatics and Biomedical to diagnose and prognoses disease Cancer. This can be further directed to a field called Machine Learning where various techniques are available to predict the cancer on the basis of collected standard data sets. The datasets may have been recorded by few repositories in the world. Only we need to apply some classifiers of Machine Learning Techniques to signify the cancer in a human. In this paper, we have surveyed the research papers to compare the accuracy of different algorithm of Machine Learning about cancer depend on the given data sets and their attributes. Several papers use very common classifier technique viz. Support Vector Machine (SVM), Random Forest (RF), Naïve Bayes (NB), Decision Tree (DT), K-Nearest Neighbor (KNN), Artificial Neural Network (ANN), Fuzzy Neural Network (FNN), Radial Basis Function Network (RBFN), Shuffled Frog Leaping with Levy Flight, Particle Swarm Optimization, Back Propagation Neural Network, Multilayered Perceptron, SVM Recursive Feature Elimination etc. In order to predict cancer disease based on the given dataset, the best result among all machine learning techniques found here is SVM.

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