Machine Learning Based Approaches for Cancer Prediction: A Survey

6 Pages Posted: 12 Apr 2019

See all articles by Ajay Kumar

Ajay Kumar

DIT University Dehradun India

Rama Sushil

DIT University, Dehradun

Arvind Kumar Tiwari

Kamla Nehru Institute of Technology

Date Written: March 11, 2019

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.

Keywords: Machine Learning, Cancer, SVM, KNN, ANN, NB

Suggested Citation

Kumar, Ajay and Sushil, Rama and Tiwari, Arvind Kumar, Machine Learning Based Approaches for Cancer Prediction: A Survey (March 11, 2019). Proceedings of 2nd International Conference on Advanced Computing and Software Engineering (ICACSE) 2019, Available at SSRN: https://ssrn.com/abstract=3350294 or http://dx.doi.org/10.2139/ssrn.3350294

Ajay Kumar (Contact Author)

DIT University Dehradun India ( email )

India

Rama Sushil

DIT University, Dehradun ( email )

Dehradun
Uttarakhand
India

Arvind Kumar Tiwari

Kamla Nehru Institute of Technology ( email )

SULTANPUR
UTTAR PRADESH
SULTANPUR
India

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