Analysis of Proposed and Traditional Boosting Algorithm with Standalone Classification Methods for Classifying Gene Expresssion Microarray Data Using a Reject Option

ABSTRACT

In medical field, accurate decisions are very important as they risk human lives. decision support system (DSS) plays important role in making accurate decisions and used for classification/prediction. In gene expression analysis, genes are not only inflated by the external environmental conditions but also the expression values of certain genes are affected (like cancer, obesity etc). in this study, various traditional (Support Vector Machine, Decision Trees, and Linear Discriminant Analysis, naïve Bayes, logistic regression, and multilayer perceptron) and proposed methods (combination of traditional with ensemble and probabilistic classifiers) are used in order to perform the classification and prediction analysis. In this study we used the publicly available datasets comprised of Lymphoid, Leukemia and Colon Cancer. The classification performance on Colon dataset with traditional methods was obtained with accuracy (56%) and proposed probabilistic ensemble methods with accuracy (88%). For dataset, Leukemia, the accuracy was obtained using traditional methods (78%) and proposed methods (92%). Similarly, on Lymphoid dataset, the traditional methods yielded accuracy (75%) and proposed methods (87%). The results revealed that proposed methods yielded the improved detection performance. The proposed methods can be used as a better predictor for early diagnosis and improved diagnosis to improve the healthcare systems.

Acknowledgments

The authors express their appreciation to “The Research Center for Advanced Materials Science (RCAMS)” at King Khalid University, Saudi Arabia, for funding this work under the grant number RCAMS/KKU/002-22.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The work was supported by the King Khalid University [RCAMS/KKU/002-22.].