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

Mir, Adil Aslam and Hussain, Lal and Waseem, Muhammad Hammad and Aldweesh, Amjad and Rasheed, Saim and Yousef, El Sayed and Nadeem, Malik Sajjad Ahmed and Eldin, Elsayed Tag (2022) Analysis of Proposed and Traditional Boosting Algorithm with Standalone Classification Methods for Classifying Gene Expresssion Microarray Data Using a Reject Option. Applied Artificial Intelligence, 36 (1). ISSN 0883-9514

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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.

Item Type: Article
Subjects: Archive Paper Guardians > Computer Science
Depositing User: Unnamed user with email support@archive.paperguardians.com
Date Deposited: 14 Jun 2023 12:05
Last Modified: 12 Dec 2023 04:32
URI: http://archives.articleproms.com/id/eprint/1250

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