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10.3390/s21103322 http://scihub22266oqcxt.onion/10.3390/s21103322 34064735!8150959!34064735     free     free     free 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       Sensors+(Basel) 2021 ; 21 (10): ä Nephropedia Template TP {{tp|p=34064735|t=2021. Future Forecasting of COVID-19: A Supervised Learning Approach |pdf=|usr=}}{{34064735}} gab.com Text Future Forecasting of COVID-19: A Supervised Learning Approach JO: Sensors (Basel) http://www.kidney.de/pget.php?&tw=1&pmid=34064735 #FOAvip A little over a year after the official announcement from the WHO, the COVID-19 pandemic has led to dramatic consequences globally. Today, millions of doses of vaccines have already been administered in several countries. However, the positive effect of these vaccines will probably be seen later than expected. In these circumstances, the rapid diagnosis of COVID-19 still remains the only way to slow the spread of this virus. However, it is difficult to predict whether a person is infected or not by COVID-19 while relying only on apparent symptoms. In this context, we propose to use machine learning (ML) algorithms in order to diagnose COVID-19 infected patients more effectively. The proposed diagnosis method takes into consideration several symptoms, such as flu symptoms, throat pain, immunity status, diarrhea, voice type, body temperature, joint pain, dry cough, vomiting, breathing problems, headache, and chest pain. Based on these symptoms that are modelled as ML features, our proposed method is able to predict the probability of contamination with the COVID-19 virus. This method is evaluated using different experimental analysis metrics such as accuracy, precision, recall, and F1-score. The obtained experimental results have shown that the proposed method can predict the presence of COVID-19 with over 97% accuracy. Twit Text FOAVip Future Forecasting of COVID-19: A Supervised Learning Approach ????Sensors (Basel) http://www.kidney.de/pget.php?&tw=1&pmid=34064735 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34064735 #FOAvip English Wikipedia <ref>{{Cite pmid|34064735}}</ref> Future Forecasting of COVID-19: A Supervised Learning Approach #MMPMID34064735 Rehman MU; Shafique A; Khalid S; Driss M; Rubaiee S Sensors (Basel) 2021[May]; 21 (10): ä PMID34064735show ga A little over a year after the official announcement from the WHO, the COVID-19 pandemic has led to dramatic consequences globally. Today, millions of doses of vaccines have already been administered in several countries. However, the positive effect of these vaccines will probably be seen later than expected. In these circumstances, the rapid diagnosis of COVID-19 still remains the only way to slow the spread of this virus. However, it is difficult to predict whether a person is infected or not by COVID-19 while relying only on apparent symptoms. In this context, we propose to use machine learning (ML) algorithms in order to diagnose COVID-19 infected patients more effectively. The proposed diagnosis method takes into consideration several symptoms, such as flu symptoms, throat pain, immunity status, diarrhea, voice type, body temperature, joint pain, dry cough, vomiting, breathing problems, headache, and chest pain. Based on these symptoms that are modelled as ML features, our proposed method is able to predict the probability of contamination with the COVID-19 virus. This method is evaluated using different experimental analysis metrics such as accuracy, precision, recall, and F1-score. The obtained experimental results have shown that the proposed method can predict the presence of COVID-19 with over 97% accuracy. |*COVID-19[MESH] |Humans[MESH] |Machine Learning[MESH] |Pandemics[MESH] |SARS-CoV-2[MESH]Future Forecasting of COVID-19: A Supervised Learning Approach (epmc).pdf DeepDyve Pubget Overpricing