  Prostate cancer is among the most common cancer in males and its
heterogeneity is well known. Its early detection helps making therapeutic
decision. There is no standard technique or procedure yet which is full-proof
in predicting cancer class. The genomic level changes can be detected in gene
expression data and those changes may serve as standard model for any random
cancer data for class prediction. Various techniques were implied on prostate
cancer data set in order to accurately predict cancer class including machine
learning techniques. Huge number of attributes and few number of sample in
microarray data leads to poor machine learning, therefore the most challenging
part is attribute reduction or non significant gene reduction. In this work we
have compared several machine learning techniques for their accuracy in
predicting the cancer class. Machine learning is effective when number of
attributes (genes) are larger than the number of samples which is rarely
possible with gene expression data. Attribute reduction or gene filtering is
absolutely required in order to make the data more meaningful as most of the
genes do not participate in tumor development and are irrelevant for cancer
prediction. Here we have applied combination of statistical techniques such as
inter-quartile range and t-test, which has been effective in filtering
significant genes and minimizing noise from data. Further we have done a
comprehensive evaluation of ten state-of-the-art machine learning techniques
for their accuracy in class prediction of prostate cancer. Out of these
techniques, Bayes Network out performed with an accuracy of 94.11% followed by
Navie Bayes with an accuracy of 91.17%. To cross validate our results, we
modified our training dataset in six different way and found that average
sensitivity, specificity, precision and accuracy of Bayes Network is highest
among all other techniques used.
