An Optimized Framework for Cancer Prediction Using Immunosignature
DOI: 10.4103/jmss.JMSS_2_18
Abstract
determination of distinct immunosignatures for various cancers has received increasing interest
recently. However, prediction accuracy and reproducibility of the computational methods are
limited. In this article, we introduce a robust method for predicting eight types of cancers including
astrocytoma, breast cancer, multiple myeloma, lung cancer, oligodendroglia, ovarian cancer, advanced
pancreatic cancer, and Ewing sarcoma. In the proposed scheme, at frst, the database is normalized
with a dictionary of normalization methods that are combined with particle swarm optimization (PSO)
for selecting the best normalization method for each feature. Then, statistical feature selection
methods are used to separate discriminative features and they were further improved by PSO with
appropriate weights as the inputs of the classifcation system. Finally, the support vector machines,
decision tree, and multilayer perceptron neural network were used as classifers. The performance of
the hybrid predictor was assessed using the holdout method. According to this method, the minimum
sensitivity, specifcity, precision, and accuracy of the proposed algorithm were 92.4 ± 1.1, 99.1 ± 1.1,
90.6 ± 2.1, and 98.3 ± 1.0, respectively, among the three types of classifcation that are used in our
algorithm. The proposed algorithm considers all the circumstances and works with each feature in
its special way. Thus, the proposed algorithm can be used as a promising framework for cancer
prediction with immunosignature.
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