Use my Search Websuite to scan PubMed, PMCentral, Journal Hosts and Journal Archives, FullText. Kick-your-searchterm to multiple Engines kick-your-query now !>

A dictionary by aggregated review articles of nephrology, medicine and the life sciences Your one-stop-run pathway from word to the immediate pdf of peer-reviewed on-topic knowledge.

10.1007/s11042-020-10340-7 http://scihub22266oqcxt.onion/10.1007/s11042-020-10340-7 33437173!7790521!33437173     free     free     free Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Multimed+Tools+Appl 2021 ; 80 (8): 11943-11957 Nephropedia Template TP {{tp|p=33437173|t=2021. Predicting the COVID-19 infection with fourteen clinical features using machine learning classification algorithms |pdf=|usr=}}{{33437173}} gab.com Text Predicting the COVID-19 infection with fourteen clinical features using machine learning classification algorithms JO: Multimed Tools Appl http://www.kidney.de/pget.php?&tw=1&pmid=33437173 #FOAvip While the RT-PCR is the silver bullet test for confirming the COVID-19 infection, it is limited by the lack of reagents, time-consuming, and the need for specialized labs. As an alternative, most of the prior studies have focused on Chest CT images and Chest X-Ray images using deep learning algorithms. However, these two approaches cannot always be used for patients' screening due to the radiation doses, high costs, and the low number of available devices. Hence, there is a need for a less expensive and faster diagnostic model to identify the positive and negative cases of COVID-19. Therefore, this study develops six predictive models for COVID-19 diagnosis using six different classifiers (i.e., BayesNet, Logistic, IBk, CR, PART, and J48) based on 14 clinical features. This study retrospected 114 cases from the Taizhou hospital of Zhejiang Province in China. The results showed that the CR meta-classifier is the most accurate classifier for predicting the positive and negative COVID-19 cases with an accuracy of 84.21%. The results could help in the early diagnosis of COVID-19, specifically when the RT-PCR kits are not sufficient for testing the infection and assist countries, specifically the developing ones that suffer from the shortage of RT-PCR tests and specialized laboratories. Twit Text FOAVip Predicting the COVID-19 infection with fourteen clinical features using machine learning classification algorithms ????Multimed Tools Appl http://www.kidney.de/pget.php?&tw=1&pmid=33437173 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33437173 #FOAvip English Wikipedia <ref>{{Cite pmid|33437173}}</ref> Predicting the COVID-19 infection with fourteen clinical features using machine learning classification algorithms #MMPMID33437173 Arpaci I; Huang S; Al-Emran M; Al-Kabi MN; Peng M Multimed Tools Appl 2021[]; 80 (8): 11943-11957 PMID33437173show ga While the RT-PCR is the silver bullet test for confirming the COVID-19 infection, it is limited by the lack of reagents, time-consuming, and the need for specialized labs. As an alternative, most of the prior studies have focused on Chest CT images and Chest X-Ray images using deep learning algorithms. However, these two approaches cannot always be used for patients' screening due to the radiation doses, high costs, and the low number of available devices. Hence, there is a need for a less expensive and faster diagnostic model to identify the positive and negative cases of COVID-19. Therefore, this study develops six predictive models for COVID-19 diagnosis using six different classifiers (i.e., BayesNet, Logistic, IBk, CR, PART, and J48) based on 14 clinical features. This study retrospected 114 cases from the Taizhou hospital of Zhejiang Province in China. The results showed that the CR meta-classifier is the most accurate classifier for predicting the positive and negative COVID-19 cases with an accuracy of 84.21%. The results could help in the early diagnosis of COVID-19, specifically when the RT-PCR kits are not sufficient for testing the infection and assist countries, specifically the developing ones that suffer from the shortage of RT-PCR tests and specialized laboratories. äPredicting the COVID-19 infection with fourteen clinical features usin(epmc).pdf DeepDyve Pubget Overpricing