General Information
    • ISSN: 1793-8201 (Print), 2972-4511 (Online)
    • Abbreviated Title: Int. J. Comput. Theory Eng.
    • Frequency: Quarterly
    • DOI: 10.7763/IJCTE
    • Editor-in-Chief: Prof. Mehmet Sahinoglu
    • Associate Editor-in-Chief: Assoc. Prof. Alberto Arteta, Assoc. Prof. Engin Maşazade
    • Managing Editor: Ms. Mia Hu
    • Abstracting/Indexing: Scopus (Since 2022), INSPEC (IET), CNKI,  Google Scholar, EBSCO, etc.
    • Average Days from Submission to Acceptance: 192 days
    • APC: 800 USD
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Prof. Mehmet Sahinoglu
Computer Science Department, Troy University, USA
I'm happy to take on the position of editor in chief of IJCTE. We encourage authors to submit papers concerning any branch of computer theory and engineering.

IJCTE 2022 Vol.14(4): 149-154 ISSN: 1793-8201
DOI: 10.7763/IJCTE.2022.V14.1322

Lung Cancer Prediction and Classification Using Adaboost Data Mining Algorithm

P. Thamilselvan
Monika Ciesielkiewicz1,*, Claire F. Bonilla2, and Carlos Olave-Lopez-de-Ayala3
1. School of Education, Computense Unversity of Madrid, Spain
2. Department of Computer Science, UDIMA—Universidad a Distancia de Madrid, Spain
3. School of Ecomomics, University of Valencia, Spain
*Corresponding author

Abstract—Currently, cancer has become a common disease which afflicts the survival of people.  Lung cancer is one of the cancer types for which early intervention and detection is especially significant. If the lung cancer is detected in the initial stages, it is easy to save the lung cancer patients from the peril of death. In this way, numerous early detection or prediction techniques are being researched and utilized in the battle against the lung cancer. Cancer is the main cause of death for the people who belong to all age groups. Early identification of the lung cancer can be useful in curing the lung disease completely. So the prerequisite of techniques to detect the occurrence of cancer is increasing. Prior analysis of the lung cancer saves huge incidents of lives to avoid other extreme issues abruptly causing deadly ends. Its prediction and cure rate depend basically on the early detection and analysis of the disease. One of the most common types of medical malpractices internationally known is blundering in the pre-determination of the disease. Information discovery and data mining have tracked down various applications in business and scientific areas. Important information can be discovered from the application of data mining techniques in a healthcare framework. In this paper, Adaboost algorithm is proposed and used to predict the lung cancer, and to find the classification accuracy in Computer Tomography (CT) Lung Images.

Index Terms—Adaboost, prediction, classification, CT images, data mining, detection, lung cancer.

P. Thamilselvan is with Dept. of Computer Science, Bishop Heber College (Autonomous) affiliated to Bharathidasan University, India (e-mail:


Cite:P. Thamilselvan, "Lung Cancer Prediction and Classification Using Adaboost Data Mining Algorithm," International Journal of Computer Theory and Engineering vol. 14, no. 4, pp. 149-154, 2022.

Copyright © 2022 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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