A comparison of cancer classification methods based on microarray data.
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Date
2018
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Abstract
Cancer is among the leading causes of death in both developed and developing
countries. Through gene expression profiling of tumors, the accuracy of cancer classification
has been enhanced, leading to correct diagnoses and the application of
effective therapies. Here, we discuss a comparative review of the binary class predictive
ability of seven classification methods (support vector machines, with the
radial basis kernel (SVM(RK)), linear kernel (SVM(LK)) and the polynomial kernel
(SVM(PK)), artificial neural networks (ANN), random forests (RF), k-nearest neighbor
(KNN), and naive Bayes (NB)), using publicly-available gene expression data
from cancer research. Results indicate that NB outperformed the other methods in
terms of the accuracy, sensitivity, specificity, kappa coefficient, area under the curve
(AUC), and balanced error rate (BER) of the binary classifier. Thus, overall the Naive
Bayes (NB) approach turned out to be the best classifier with our datasets.
Description
Masters Degree. University of KwaZulu-Natal, Pietermaritzburg.