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10.26355/eurrev_202003_20709 http://scihub22266oqcxt.onion/10.26355/eurrev_202003_20709 32271458!ä!32271458 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Eur+Rev+Med+Pharmacol+Sci 2020 ; 24 (6): 3400-3403 Nephropedia Template TP {{tp|p=32271458|t=2020. A classifier prediction model to predict the status of Coronavirus COVID-19 patients in South Korea |pdf=|usr=}}{{32271458}} gab.com Text A classifier prediction model to predict the status of Coronavirus COVID-19 patients in South Korea JO: Eur Rev Med Pharmacol Sci http://www.kidney.de/pget.php?&tw=1&pmid=32271458 #FOAvip OBJECTIVE: Coronavirus COVID-19 further transmitted to several countries globally. The status of the infected cases can be determined basing on the treatment process along with several other factors. This research aims to build a classifier prediction model to predict the status of recovered and death coronavirus CovID-19 patients in South Korea. MATERIALS AND METHODS: Artificial neural network principle is used to classify the collected data between February 20, 2020 and March 9, 2020. The proposed classifier used different seven variables, namely, country, infection reason, sex, group, confirmation date, birth year, and region. The most effective variables on recovered and fatal cases are analyzed based on the neural network model. RESULTS: The results found that the proposed predictive classifier efficiently predicted recovered and death cases. Besides, it is found that discovering the infection reason would increase the probability to recover the patient. This indicates that the virus might be controllable based on infection reasons. In addition, the earlier discovery of the disease affords better control and a higher probability of being recovered. CONCLUSIONS: Our recommendation is to use this model to predict the status of the patients globally. Twit Text FOAVip A classifier prediction model to predict the status of Coronavirus COVID-19 patients in South Korea ????Eur Rev Med Pharmacol Sci http://www.kidney.de/pget.php?&tw=1&pmid=32271458 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32271458 #FOAvip English Wikipedia <ref>{{Cite pmid|32271458}}</ref> A classifier prediction model to predict the status of Coronavirus COVID-19 patients in South Korea #MMPMID32271458 Al-Najjar H; Al-Rousan N Eur Rev Med Pharmacol Sci 2020[Mar]; 24 (6): 3400-3403 PMID32271458show ga OBJECTIVE: Coronavirus COVID-19 further transmitted to several countries globally. The status of the infected cases can be determined basing on the treatment process along with several other factors. This research aims to build a classifier prediction model to predict the status of recovered and death coronavirus CovID-19 patients in South Korea. MATERIALS AND METHODS: Artificial neural network principle is used to classify the collected data between February 20, 2020 and March 9, 2020. The proposed classifier used different seven variables, namely, country, infection reason, sex, group, confirmation date, birth year, and region. The most effective variables on recovered and fatal cases are analyzed based on the neural network model. RESULTS: The results found that the proposed predictive classifier efficiently predicted recovered and death cases. Besides, it is found that discovering the infection reason would increase the probability to recover the patient. This indicates that the virus might be controllable based on infection reasons. In addition, the earlier discovery of the disease affords better control and a higher probability of being recovered. CONCLUSIONS: Our recommendation is to use this model to predict the status of the patients globally. |*Betacoronavirus[MESH] |COVID-19[MESH] |COVID-19 Testing[MESH] |Clinical Laboratory Techniques[MESH] |Coronavirus Infections/diagnosis/*epidemiology/prevention & control[MESH] |Disease Outbreaks[MESH] |Health Education[MESH] |Humans[MESH] |Pandemics/prevention & control[MESH] |Pneumonia, Viral/diagnosis/*epidemiology/prevention & control[MESH] |Republic of Korea/epidemiology[MESH]A classifier prediction model to predict the status of Coronavirus COV(epmc).pdf DeepDyve Pubget Overpricing