LASSO-DT Based Classification Technique for Discovery of COVID-19 Disease Using Chest X-Ray Images

Roseline Oluwaseun Ogundokun (), Joseph Bamidele Awotunde (), Paul Onawola () and Taye Oladele Aro ()
Additional contact information
Roseline Oluwaseun Ogundokun: Landmark University
Joseph Bamidele Awotunde: University of Ilorin
Paul Onawola: Landmark University
Taye Oladele Aro: Koladaisi University

Chapter Chapter 23 in Decision Sciences for COVID-19, 2022, pp 407-422 from Springer

Abstract: Abstract Early recognition of COVID-19 can aid in the development of a treatment plan and disease containment decisions. This research aims to use the LASSO+DT technique to automatically diagnose COVID19 lung-related individuals employing automated chest X-ray imageries while optimizing detection accuracy. The study employed machine learning (ML) based techniques, specifically feature selection (FS) and classification models were used to classify COVID-19 and normal chest X-ray imageries. For the FS, Least Absolute Shrinkage and Selection Operator (LASSO) technique was employed and for the classification, a Decision Tree (DT) classifier was used. A dataset containing 372 instances of chest X-rays was employed in this research for the investigation. The classification accuracy, detection rate (DR), and false-positive rate (FPR) were employed for the performance evaluation of the investigation. The study implemented DT alone as well as LASSO+DT and the results of both classifiers were compared. The result of the study recognized that the proposed LASSO+DT outperformed that of the DT alone. The investigation demonstrates that the proposed LASSO+DT is efficient and effective for the identification of COVID-19 ailment and further lung diseases because it has higher accuracy, DR, and lower FAR when compared with DT technique and few state of the art.

Keywords: COVID-19; LASSO; Decision tree; Feature selection; Chest X-ray (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:isochp:978-3-030-87019-5_23

Ordering information: This item can be ordered from
http://www.springer.com/9783030870195

DOI: 10.1007/978-3-030-87019-5_23

Access Statistics for this chapter

More chapters in International Series in Operations Research & Management Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().