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المؤلفTawsifur, Rahman
المؤلفKhandakar, Amith
المؤلفAbir, Farhan Fuad
المؤلفFaisal, Md Ahasan Atick
المؤلفHossain, Md Shafayet
المؤلفPodder, Kanchon Kanti
المؤلفAbbas, Tariq O.
المؤلفAlam, Mohammed Fasihul
المؤلفKashem, Saad Bin
المؤلفIslam, Mohammad Tariqul
المؤلفZughaier, Susu M.
المؤلفChowdhury, Muhammad E.H.
تاريخ الإتاحة2022-02-20T06:55:36Z
تاريخ النشر2022-04-30
اسم المنشورComputers in Biology and Medicine
المعرّفhttp://dx.doi.org/10.1016/j.compbiomed.2022.105284
الاقتباسTawsifur Rahman, Amith Khandakar, Farhan Fuad Abir, Md Ahasan Atick Faisal, Md Shafayet Hossain, Kanchon Kanti Podder, Tariq O. Abbas, Mohammed Fasihul Alam, Saad Bin Kashem, Mohammad Tariqul Islam, Susu M. Zughaier, Muhammad E.H. Chowdhury, QCovSML: A reliable COVID-19 detection system using CBC biomarkers by a stacking machine learning model, Computers in Biology and Medicine, Volume 143, 2022, 105284, ISSN 0010-4825, https://doi.org/10.1016/j.compbiomed.2022.105284.
الرقم المعياري الدولي للكتاب00104825
المعرّف105284
معرّف المصادر الموحدhttps://www.sciencedirect.com/science/article/pii/S0010482522000762
معرّف المصادر الموحدhttp://hdl.handle.net/10576/27289
الملخصThe reverse transcription-polymerase chain reaction (RT-PCR) test is considered the current gold standard for the detection of coronavirus disease (COVID-19), although it suffers from some shortcomings, namely comparatively longer turnaround time, higher false-negative rates around 20–25%, and higher cost equipment. Therefore, finding an efficient, robust, accurate, and widely available, and accessible alternative to RT-PCR for COVID-19 diagnosis is a matter of utmost importance. This study proposes a complete blood count (CBC) biomarkers-based COVID-19 detection system using a stacking machine learning (SML) model, which could be a fast and less expensive alternative. This study used seven different publicly available datasets, where the largest one consisting of fifteen CBC biomarkers collected from 1624 patients (52% COVID-19 positive) admitted at San Raphael Hospital, Italy from February to May 2020 was used to train and validate the proposed model. White blood cell count, monocytes (%), lymphocyte (%), and age parameters collected from the patients during hospital admission were found to be important biomarkers for COVID-19 disease prediction using five different feature selection techniques. Our stacking model produced the best performance with weighted precision, sensitivity, specificity, overall accuracy, and F1-score of 91.44%, 91.44%, 91.44%, 91.45%, and 91.45%, respectively. The stacking machine learning model improved the performance in comparison to other state-of-the-art machine learning classifiers. Finally, a nomogram-based scoring system (QCovSML) was constructed using this stacking approach to predict the COVID-19 patients. The cut-off value of the QCovSML system for classifying COVID-19 and Non-COVID patients was 4.8. Six datasets from three different countries were used to externally validate the proposed model to evaluate its generalizability and robustness. The nomogram demonstrated good calibration and discrimination with the area under the curve (AUC) of 0.961 for the internal cohort and average AUC of 0.967 for all external validation cohort, respectively. The external validation shows an average weighted precision, sensitivity, F1-score, specificity, and overall accuracy of 92.02%, 95.59%, 93.73%, 90.54%, and 93.34%, respectively.
اللغةen
الناشرElsevier
الموضوعCOVID-19
Detection
Complete blood count (CBC)
Stacking machine learning
RT-PCR
العنوانQCovSML: A reliable COVID-19 detection system using CBC biomarkers by a stacking machine learning model
النوعArticle
رقم المجلد143
dc.accessType Open Access


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