Automatic Classification Between COVID-19 and Non-COVID-19 Pneumonia Using Symptoms, Comorbidities, and Laboratory Findings: The Khorshid COVID Cohort Study
Coronavirus disease-2019, also known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was a disaster in 2020. Accurate and early diagnosis of coronavirus disease-2019 (COVID-19) is still essential for health policymaking. Reverse transcriptase-polymerase chain reaction (RT-PCR) has b...
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2021
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oai:doaj.org-article:804d92ead4774e318a1732aaa6c223432021-11-19T10:44:14ZAutomatic Classification Between COVID-19 and Non-COVID-19 Pneumonia Using Symptoms, Comorbidities, and Laboratory Findings: The Khorshid COVID Cohort Study2296-858X10.3389/fmed.2021.768467https://doaj.org/article/804d92ead4774e318a1732aaa6c223432021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fmed.2021.768467/fullhttps://doaj.org/toc/2296-858XCoronavirus disease-2019, also known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was a disaster in 2020. Accurate and early diagnosis of coronavirus disease-2019 (COVID-19) is still essential for health policymaking. Reverse transcriptase-polymerase chain reaction (RT-PCR) has been performed as the operational gold standard for COVID-19 diagnosis. We aimed to design and implement a reliable COVID-19 diagnosis method to provide the risk of infection using demographics, symptoms and signs, blood markers, and family history of diseases to have excellent agreement with the results obtained by the RT-PCR and CT-scan. Our study primarily used sample data from a 1-year hospital-based prospective COVID-19 open-cohort, the Khorshid COVID Cohort (KCC) study. A sample of 634 patients with COVID-19 and 118 patients with pneumonia with similar characteristics whose RT-PCR and chest CT scan were negative (as the control group) (dataset 1) was used to design the system and for internal validation. Two other online datasets, namely, some symptoms (dataset 2) and blood tests (dataset 3), were also analyzed. A combination of one-hot encoding, stability feature selection, over-sampling, and an ensemble classifier was used. Ten-fold stratified cross-validation was performed. In addition to gender and symptom duration, signs and symptoms, blood biomarkers, and comorbidities were selected. Performance indices of the cross-validated confusion matrix for dataset 1 were as follows: sensitivity of 96% [confidence interval, CI, 95%: 94–98], specificity of 95% [90–99], positive predictive value (PPV) of 99% [98–100], negative predictive value (NPV) of 82% [76–89], diagnostic odds ratio (DOR) of 496 [198–1,245], area under the ROC (AUC) of 0.96 [0.94–0.97], Matthews Correlation Coefficient (MCC) of 0.87 [0.85–0.88], accuracy of 96% [94–98], and Cohen's Kappa of 0.86 [0.81–0.91]. The proposed algorithm showed excellent diagnosis accuracy and class-labeling agreement, and fair discriminant power. The AUC on the datasets 2 and 3 was 0.97 [0.96–0.98] and 0.92 [0.91–0.94], respectively. The most important feature was white blood cell count, shortness of breath, and C-reactive protein for datasets 1, 2, and 3, respectively. The proposed algorithm is, thus, a promising COVID-19 diagnosis method, which could be an amendment to simple blood tests and screening of symptoms. However, the RT-PCR and chest CT-scan, performed as the gold standard, are not 100% accurate.Hamid Reza MaratebFarzad Ziaie NezhadMohammad Reza MohebianRamin SamiShaghayegh Haghjooy JavanmardFatemeh Dehghan NiriMahsa Akafzadeh-SavariMarjan MansourianMarjan MansourianMiquel Angel MañanasMiquel Angel MañanasMartin WolkewitzHarald BinderFrontiers Media S.A.articleCOVID-19computer-aided diagnosisscreeningvalidation studiesmachine learningMedicine (General)R5-920ENFrontiers in Medicine, Vol 8 (2021) |
| institution |
DOAJ |
| collection |
DOAJ |
| language |
EN |
| topic |
COVID-19 computer-aided diagnosis screening validation studies machine learning Medicine (General) R5-920 |
| spellingShingle |
COVID-19 computer-aided diagnosis screening validation studies machine learning Medicine (General) R5-920 Hamid Reza Marateb Farzad Ziaie Nezhad Mohammad Reza Mohebian Ramin Sami Shaghayegh Haghjooy Javanmard Fatemeh Dehghan Niri Mahsa Akafzadeh-Savari Marjan Mansourian Marjan Mansourian Miquel Angel Mañanas Miquel Angel Mañanas Martin Wolkewitz Harald Binder Automatic Classification Between COVID-19 and Non-COVID-19 Pneumonia Using Symptoms, Comorbidities, and Laboratory Findings: The Khorshid COVID Cohort Study |
| description |
Coronavirus disease-2019, also known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was a disaster in 2020. Accurate and early diagnosis of coronavirus disease-2019 (COVID-19) is still essential for health policymaking. Reverse transcriptase-polymerase chain reaction (RT-PCR) has been performed as the operational gold standard for COVID-19 diagnosis. We aimed to design and implement a reliable COVID-19 diagnosis method to provide the risk of infection using demographics, symptoms and signs, blood markers, and family history of diseases to have excellent agreement with the results obtained by the RT-PCR and CT-scan. Our study primarily used sample data from a 1-year hospital-based prospective COVID-19 open-cohort, the Khorshid COVID Cohort (KCC) study. A sample of 634 patients with COVID-19 and 118 patients with pneumonia with similar characteristics whose RT-PCR and chest CT scan were negative (as the control group) (dataset 1) was used to design the system and for internal validation. Two other online datasets, namely, some symptoms (dataset 2) and blood tests (dataset 3), were also analyzed. A combination of one-hot encoding, stability feature selection, over-sampling, and an ensemble classifier was used. Ten-fold stratified cross-validation was performed. In addition to gender and symptom duration, signs and symptoms, blood biomarkers, and comorbidities were selected. Performance indices of the cross-validated confusion matrix for dataset 1 were as follows: sensitivity of 96% [confidence interval, CI, 95%: 94–98], specificity of 95% [90–99], positive predictive value (PPV) of 99% [98–100], negative predictive value (NPV) of 82% [76–89], diagnostic odds ratio (DOR) of 496 [198–1,245], area under the ROC (AUC) of 0.96 [0.94–0.97], Matthews Correlation Coefficient (MCC) of 0.87 [0.85–0.88], accuracy of 96% [94–98], and Cohen's Kappa of 0.86 [0.81–0.91]. The proposed algorithm showed excellent diagnosis accuracy and class-labeling agreement, and fair discriminant power. The AUC on the datasets 2 and 3 was 0.97 [0.96–0.98] and 0.92 [0.91–0.94], respectively. The most important feature was white blood cell count, shortness of breath, and C-reactive protein for datasets 1, 2, and 3, respectively. The proposed algorithm is, thus, a promising COVID-19 diagnosis method, which could be an amendment to simple blood tests and screening of symptoms. However, the RT-PCR and chest CT-scan, performed as the gold standard, are not 100% accurate. |
| format |
article |
| author |
Hamid Reza Marateb Farzad Ziaie Nezhad Mohammad Reza Mohebian Ramin Sami Shaghayegh Haghjooy Javanmard Fatemeh Dehghan Niri Mahsa Akafzadeh-Savari Marjan Mansourian Marjan Mansourian Miquel Angel Mañanas Miquel Angel Mañanas Martin Wolkewitz Harald Binder |
| author_facet |
Hamid Reza Marateb Farzad Ziaie Nezhad Mohammad Reza Mohebian Ramin Sami Shaghayegh Haghjooy Javanmard Fatemeh Dehghan Niri Mahsa Akafzadeh-Savari Marjan Mansourian Marjan Mansourian Miquel Angel Mañanas Miquel Angel Mañanas Martin Wolkewitz Harald Binder |
| author_sort |
Hamid Reza Marateb |
| title |
Automatic Classification Between COVID-19 and Non-COVID-19 Pneumonia Using Symptoms, Comorbidities, and Laboratory Findings: The Khorshid COVID Cohort Study |
| title_short |
Automatic Classification Between COVID-19 and Non-COVID-19 Pneumonia Using Symptoms, Comorbidities, and Laboratory Findings: The Khorshid COVID Cohort Study |
| title_full |
Automatic Classification Between COVID-19 and Non-COVID-19 Pneumonia Using Symptoms, Comorbidities, and Laboratory Findings: The Khorshid COVID Cohort Study |
| title_fullStr |
Automatic Classification Between COVID-19 and Non-COVID-19 Pneumonia Using Symptoms, Comorbidities, and Laboratory Findings: The Khorshid COVID Cohort Study |
| title_full_unstemmed |
Automatic Classification Between COVID-19 and Non-COVID-19 Pneumonia Using Symptoms, Comorbidities, and Laboratory Findings: The Khorshid COVID Cohort Study |
| title_sort |
automatic classification between covid-19 and non-covid-19 pneumonia using symptoms, comorbidities, and laboratory findings: the khorshid covid cohort study |
| publisher |
Frontiers Media S.A. |
| publishDate |
2021 |
| url |
https://doaj.org/article/804d92ead4774e318a1732aaa6c22343 |
| work_keys_str_mv |
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