Application of intelligence-based computational techniques for classification and early differential diagnosis of COVID-19 disease

Data Science and Management - Tập 4 - Trang 10-18 - 2021
Boluwaji A. Akinnuwesi1, Stephen G. Fashoto1, Elliot Mbunge1, Adedoyin Odumabo2, Andile S. Metfula1, Petros Mashwama1, Faith-Michael Uzoka3, Olumide Owolabi4, Moses Okpeku5, Oluwaseun O. Amusa6
1Department of Computer Science, Faculty of Science and Engineering, University of Eswatini, (Formerly University of Swaziland), Kwaluseni M201, Manzini, Eswatini, Swaziland
2Department of Computer Science, Faculty of Science, Lagos State University, Ojo, Lagos State, 102105, Nigeria
3Department of Computer Science and Information Systems, Mount Royal University, Calgary, Alberta, T3E6K6, Canada
4Department of Computer Science, University of Abuja, 900105, Nigeria
5Department of Genetics, University of KwaZulu-Natal, 4041, South Africa
6Department of English Studies, Faculty of Arts, Adekunle Ajasin University, Akungba-Akoko, Ondo State, 342111, Nigeria

Tài liệu tham khảo

Alakus, 2020, Comparison of deep learning approaches to predict COVID-19 infection, Chaos, Solit. Fractals, 140, 1 Alpdagtas, 2020, Evaluation of current diagnostic methods for COVID-19, APL Bioeng., 4, 10.1063/5.0021554 Alqudah, 2020, COVID-19 detection from x-ray images using different artificial intelligence hybrid models, Jordan J. Elect. Eng., 6, 168, 10.5455/jjee.204-1585312246 Anne Ardakani, 2021, COVIDiag: a clinical CAD system to diagnose COVID-19 pneumonia based on CT findings, Eur. Radiol., 31, 121, 10.1007/s00330-020-07087-y Ardakani, 2020, Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: results of 10 convolutional neural networks, Comput. Biol. Med., 121, 1 Arevalo-Rodriguez, 2020, False-negative results of initial RT-PCR assays for COVID-19: a systematic review, PLoS One., 15, 10.1371/journal.pone.0242958 Atieh, 2019, Predicting peri-implant disease: Chi-square Automatic Interaction Detection (CHAID) decision tree analysis of risk indicators, J. Periodontol., 90, 834, 10.1002/JPER.17-0501 Awwalu, 2020, A multinomial naïve Bayes decision support system for covid-19 detection, Fudma J. Sci., 4, 704, 10.33003/fjs-2020-0402-331 Bhandari, 2020, Logistic regression analysis to predict mortality risk in COVID-19 patients from routine hematologic parameters, Ibnosina J. Med. Biomed. Sci., 12, 123, 10.4103/ijmbs.ijmbs_58_20 Biswal, 2021, Top 10 deep learning algorithms You should Know in 2021, Simplilearn. Brownlee, 2016, Logistic regression for machine learning, Mach. Learn. Alg. Caliendo Canayaz, 2021, MH-COVIDNet: diagnosis of COVID-19 using deep neural networks and meta-heuristic-based feature selection on X-ray images, Biomed. Signal Process Control, 10.1016/j.bspc.2020.102257 Chandiok, 2016, Cognitive decision support system for medical diagnosis Chicco, 2020, The advantages of the Matthews Correlation Coefficient (MCC) over F1 score and accuracy in binary classification evaluation, BMC Genom., 21, 1, 10.1186/s12864-019-6413-7 Chicco, 2021, The Matthews Correlation Coefficient (MCC) is more reliable than balanced accuracy, bookmaker informedness, and markedness in two-class confusion matrix evaluation, BioData Min., 14, 1, 10.1186/s13040-021-00244-z Dai, 2020, CT imaging and differential diagnosis of COVID-19, Can. Assoc. Radiol. J., 71, 195, 10.1177/0846537120913033 de Moraes Lopes, 2013, Fuzzy cognitive map in differential diagnosis of alterations in urinary elimination: a nursing approach, Int. J. Med. Inf., 82, 201, 10.1016/j.ijmedinf.2012.05.012 Dinant Elujide, 2021, Application of deep and machine learning techniques for multi-label classification performance on psychotic disorder diseases, Inf. Med. Unlocked, 23, 100545, 10.1016/j.imu.2021.100545 Fang, 2020, Sensitivity of chest CT for COVID-19: comparison to RT-PCR, Radiology., 296, E115, 10.1148/radiol.2020200432 Fink, 2020, Development and internal validation of a diagnostic prediction model for COVID-19 at time of admission to hospital, QJM: Monthly J. Assoc. Phys., 114, 699, 10.1093/qjmed/hcaa305 Fleitas, 2020, Understanding the value of clinical symptoms of COVID-19. A logistic regression model, medRxiv. Giri, 2020, Review of analytical performance of COVID-19 detection methods, Anal. Bioanal. Chem., 413, 35 Gour, 2020, Stacked convolutional neural network for diagnosis of covid-19 disease from x-ray images, Preprints. Groumpos, 2020, A new mathematical modell for COVID-19: a fuzzy cognitive map approach for coronavirus diseases Gupta Hammam, 2020, Stacking deep learning for early COVID-19 vision diagnosis, 297 Hassantabar, 2020, Diagnosis and detection of infected tissue of COVID-19 patients based on lung X-ray image using convolutional neural network approaches, Chaos, Solit. Fractals, 140, 1 He, 2020, Diagnostic performance between CT and initial real-time RT-PCR for clinically suspected 2019 coronavirus disease (COVID-19) patients outside Wuhan, China, Respir. Med., 168, 105980, 10.1016/j.rmed.2020.105980 Iwendi, 2020, COVID-19 patient health prediction using boosted random forest algorithm, Frontiers in public health, 8, 1 Jain, 2017, The key role of differential diagnosis in diagnosis, Diagnosis, 4, 239, 10.1515/dx-2017-0005 Jin, 2021, Hybrid ensemble model for differential diagnosis between COVID-19 and common viral pneumonia by chest X-ray radiograph, Comput. Biol. Med., 131, 1 Kamadi, 2016, A computational intelligence technique for the effective diagnosis of diabetic patients using principal component analysis (PCA) and modified fuzzy SLIQ decision tree approach, Appl. Soft Comput., 49, 137, 10.1016/j.asoc.2016.05.010 Khan, 2020, CoroNet: a deep neural network for detection and diagnosis of COVID-19 from chest x-ray images, Comput. Methods Progr. Biomed., 196, 1 Kosko, 1986, Fuzzy cognitive maps, Int. J. Man Mach. Stud., 24, 65, 10.1016/S0020-7373(86)80040-2 Land, 2020, The support vector machine, 45 Lee, 2020, Testing for SARS-CoV-2: can we stop at 2?, Clin. Infect. Dis., 71, 2246, 10.1093/cid/ciaa459 Lieberman, 2020, Comparison of commercially available and laboratory-developed assays for in vitro detection of SARS-CoV-2 in clinical laboratories, J. Clin. Microbiol., 58, 1, 10.1128/JCM.00821-20 Long, 2020, Diagnosis of the Coronavirus disease (COVID-19): rRT-PCR or CT?, Eur. J. Radiol., 126, 1 Long, 2020, Occurrence and timing of subsequent SARS-CoV-2 RT-PCR positivity among initially negative patients, Clin. Infect. Dis., 72, 323 Luo, 2020, Utility of chest CT in diagnosis of COVID-19 pneumonia, Diagn. Interventional Radiol., 26, 437, 10.5152/dir.2020.20144 Mahapatra, 2020, Clinically practiced and commercially viable nanobio engineered analytical methods for COVID-19 diagnosis, Biosens. Bioelectron., 165, 112361, 10.1016/j.bios.2020.112361 Mahmud, 2020, CovXNet: a multi-dilation convolutional neural network for automatic COVID-19 and other pneumonia detection from chest X-ray images with transferable multi-receptive feature optimization, Comput. Biol. Med., 122, 1 Mann, 1990, Differential diagnosis and classification of apathy, Am. J. Psychiatr., 147, 22, 10.1176/ajp.147.1.22 Mansour, 2022, Accurate detection of covid-19 patients based on Feature Correlated Naïve Bayes (FCNB) classification strategy, J. Amb. Intell. Humanized Comput., 13 (Jan.), 41, 10.1007/s12652-020-02883-2 Mbunge, 2021, Ethics for integrating emerging technologies to contain COVID-19 in Zimbabwe, Human Behav. Emerg. Technol, 10.1002/hbe2.277 McIntosh Mertens, 2020, Development and potential usefulness of the COVID-19 Ag respi-strip diagnostic assay in a pandemic context, Front. Med., 7, 1 Mohammadi, 2021, Artificial neural network and logistic regression modelling to characterize COVID-19 infected patients in local areas of Iran, Biomed. J., 44, 304, 10.1016/j.bj.2021.02.006 Mukherjee, 2020, Deep neural network to detect COVID-19: one architecture for both CT Scans and Chest X-rays, Appl. Intell. Murthy, 2020, Differential diagnosis of acute ocular pain: teleophthalmology during COVID-19 pandemic-a perspective, Indian J. Ophthalmol., 68, 1371, 10.4103/ijo.IJO_1267_20 Nalla, 2020, Comparative performance of SARS-CoV-2 detection assays using seven different primer-probe sets and one assay kit, J. Clin. Microbiol., 58, 1, 10.1128/JCM.00557-20 Noble, 2006, What is a support vector machine?, Nat. Biotechnol., 24, 1565, 10.1038/nbt1206-1565 Oh, 2020, Deep learning covid-19 features on cxr using limited training data sets, IEEE Trans. Med. Imag., 39, 2688, 10.1109/TMI.2020.2993291 Papageorgiou, 2008, Fuzzy cognitive maps, Handbook Granular Comput., 123, 755, 10.1002/9780470724163.ch34 Pisner, 2020, Support vector machine, 101 Ray Roland, 2020, Smell and taste symptom-based predictive model for COVID-19 diagnosis Rutledge Salman, 2020, Covid-19 detection using artificial intelligence, Int. J. Appl. Eng. Res., 4, 18 Sand, 2015, Classification, diagnosis, and differential diagnosis of multiple sclerosis, Curr. Opin. Neurol., 28, 193, 10.1097/WCO.0000000000000206 Sethy, 2020, Detection of coronavirus disease (COVID-19) based on deep features and support vector machine, Preprints Shaban, 2021, Detecting COVID-19 patients based on fuzzy inference engine and Deep Neural Network, Appl. Soft Comput., 99, 106906, 10.1016/j.asoc.2020.106906 Shang, 2020, The value of clinical parameters in predicting the severity of COVID-19, J. Med. Virol., 92, 2188, 10.1002/jmv.26031 Silahudin, 2020, Model expert system for diagnosis of covid-19 using naïve Bayes classifier Singh, 2021, COVIDScreen: explainable deep learning framework for differential diagnosis of COVID-19 using chest X-Rays, Neural Comput. Appl., 10, 1 Song, 2020, Analysis of prediction and early warning indexes of patients with COVID-19, Expet Rev. Respir. Med., 14, 1257, 10.1080/17476348.2020.1793674 Sun, 2020, A prediction model based on machine learning for diagnosing the early COVID-19 patients, medRxiv Udugama, 2020, Diagnosing COVID-19: the disease and tools for detection, ACS Nano, 14, 3822, 10.1021/acsnano.0c02624 Uygun-Can, 2020, Clinical properties and diagnostic methods of COVID-19 infection in pregnancies: meta-analysis, BioMed Res. Int., 10.1155/2020/1708267 Uzoka, 2016, A framework for early differential diagnosis of tropical confusable diseases using the fuzzy cognitive map engine, World Acad. Sci. Eng. Technol. Int. J. Comput. Elect. Automat. Control Inf. Eng., 10, 346 Wan, 2020, Current practice and potential strategy in diagnosing COVID-19, Eur. Rev. Med. Pharmacol. Sci., 24, 4548 Weissleder, 2020, COVID-19 diagnostics in context, Sci. Transl. Med., 12, 1, 10.1126/scitranslmed.abc1931 Wu, 2020, The diagnostic methods in the COVID-19 pandemic, today and in the future, Expert Rev. Mol. Diagn., 20, 985, 10.1080/14737159.2020.1816171 Xie, 2020, Characteristics of patients with coronavirus disease (COVID-19) confirmed using an IgM-IgG antibody test, J. Med. Virol., 92, 2004, 10.1002/jmv.25930 Xu, 2020, Application of ordinal logistic regression analysis to identify the determinants of illness severity of COVID-19 in China, Epidemiol. Infect., 148, 1 Yang, 2020, Point-of-Care RNA-based diagnostic device for COVID-19, Diagnostics., 10, 1 Yoo, 2020, Deep learning-based decision-tree classifier for COVID-19 diagnosis from chest X-ray imaging, Front. Med., 7, 1 Yuan, 2020, Current and perspective diagnostic techniques for COVID-19, ACS Infect. Dis., 6, 1998, 10.1021/acsinfecdis.0c00365 Zeng, 2020, Differential diagnosis of COVID-19 pneumonia in cancer patients received radiotherapy, Int. J. Med. Sci., 17, 2561, 10.7150/ijms.46133 Zuo, 2020, Contribution of CT features in the diagnosis of COVID-19, Can. Respir. J. J. Can. Thorac. Soc., 10, 1