
International Journal For Multidisciplinary Research
E-ISSN: 2582-2160
•
Impact Factor: 9.24
A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal
Home
Research Paper
Submit Research Paper
Publication Guidelines
Publication Charges
Upload Documents
Track Status / Pay Fees / Download Publication Certi.
Editors & Reviewers
View All
Join as a Reviewer
Get Membership Certificate
Current Issue
Publication Archive
Conference
Publishing Conf. with IJFMR
Upcoming Conference(s) ↓
WSMCDD-2025
GSMCDD-2025
AIMAR-2025
Conferences Published ↓
ICCE (2025)
RBS:RH-COVID-19 (2023)
ICMRS'23
PIPRDA-2023
Contact Us
Plagiarism is checked by the leading plagiarism checker
Call for Paper
Volume 7 Issue 4
July-August 2025
Indexing Partners



















Artificial Intelligence Approaches for COVID-19 Detection Using Boosting Algorithms
Author(s) | Ms. Chitra Devi Thangavelu, Ms. Abinaya R, Ms. Dhanvandhini S, Mr. Sivakumar R |
---|---|
Country | India |
Abstract | The ongoing global impact of the COVID-19 pandemic has underscored the need for rapid, accurate, and accessible diagnostic tools. In this study, we present an artificial intelligence (AI)-driven framework for the diagnosis of COVID-19 using four state-of-the-art boosting-based machine learning algorithms: Extreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), Gradient Boosting, and Categorical Boosting (CatBoost). These models were trained and evaluated using a dataset comprising both clinical and demographic features of patients, enabling the identification of infection status based on readily available health indicators. The evaluation of model performance was conducted using standard classification metrics such as accuracy, recall, F1-score, and Area Under the Receiver Operating Characteristic Curve (AUC-ROC). Among the models tested, CatBoost and XGBoost demonstrated superior recall and AUC values, making them especially valuable in minimizing false negatives a critical factor in disease detection scenarios where missed cases can lead to further transmission and delayed treatment. The study emphasizes that boosting algorithms, particularly CatBoost and XGBoost, are not only accurate but also computationally efficient and well-suited for handling structured tabular data. Their effectiveness in this application supports their potential integration into clinical decision support systems, especially in resource-constrained healthcare environments where diagnostic capabilities are limited. Overall, the findings validate the utility of boosting-based AI models as robust, scalable, and practical solutions for enhancing early COVID-19 detection and aiding frontline medical practitioners in timely decision-making. |
Keywords | COVID-19 Diagnosis, Machine Learning, Boosting Algorithms, XGBoost, AdaBoost, Gradient Boosting, Binary Classification, CatBoost |
Field | Computer > Artificial Intelligence / Simulation / Virtual Reality |
Published In | Volume 7, Issue 4, July-August 2025 |
Published On | 2025-07-27 |
DOI | https://doi.org/10.36948/ijfmr.2025.v07i04.51705 |
Short DOI | https://doi.org/g9vpkm |
Share this

E-ISSN 2582-2160

CrossRef DOI is assigned to each research paper published in our journal.
IJFMR DOI prefix is
10.36948/ijfmr
Downloads
All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.
