A Precise Method to Detect Post-COVID-19 Pulmonary Fibrosis Through Extreme Gradient Boosting.
SN Comput Sci
; 4(1): 89, 2023.
Article
in English
| MEDLINE | ID: covidwho-2158269
ABSTRACT
The association of pulmonary fibrosis with COVID-19 patients has now been adequately acknowledged and caused a significant number of mortalities around the world. As automatic disease detection has now become a crucial assistant to clinicians to obtain fast and precise results, this study proposes an architecture based on an ensemble machine learning approach to detect COVID-19-associated pulmonary fibrosis. The paper discusses Extreme Gradient Boosting (XGBoost) and its tuned hyper-parameters to optimize the performance for the prediction of severe COVID-19 patients who developed pulmonary fibrosis after 90 days of hospital discharge. A dataset comprising Electronic Health Record (EHR) and corresponding High-resolution computed tomography (HRCT) images of chest of 1175 COVID-19 patients has been considered, which involves 725 pulmonary fibrosis cases and 450 normal lung cases. The experimental results achieved an accuracy of 98%, precision of 99% and sensitivity of 99%. The proposed model is the first in literature to help clinicians in keeping a record of severe COVID-19 cases for analyzing the risk of pulmonary fibrosis through EHRs and HRCT scans, leading to less chance of life-threatening conditions.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Type of study:
Diagnostic study
/
Prognostic study
Topics:
Long Covid
Language:
English
Journal:
SN Comput Sci
Year:
2023
Document Type:
Article
Affiliation country:
S42979-022-01526-x
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