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10.3389/fpubh.2020.00357 http://scihub22266oqcxt.onion/10.3389/fpubh.2020.00357 32719767!7350612!32719767     free     free     free Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Front+Public+Health 2020 ; 8 (ä): 357 Nephropedia Template TP {{tp|p=32719767|t=2020. COVID-19 Patient Health Prediction Using Boosted Random Forest Algorithm |pdf=|usr=}}{{32719767}} gab.com Text COVID-19 Patient Health Prediction Using Boosted Random Forest Algorithm JO: Front Public Health http://www.kidney.de/pget.php?&tw=1&pmid=32719767 #FOAvip Integration of artificial intelligence (AI) techniques in wireless infrastructure, real-time collection, and processing of end-user devices is now in high demand. It is now superlative to use AI to detect and predict pandemics of a colossal nature. The Coronavirus disease 2019 (COVID-19) pandemic, which originated in Wuhan China, has had disastrous effects on the global community and has overburdened advanced healthcare systems throughout the world. Globally; over 4,063,525 confirmed cases and 282,244 deaths have been recorded as of 11th May 2020, according to the European Centre for Disease Prevention and Control agency. However, the current rapid and exponential rise in the number of patients has necessitated efficient and quick prediction of the possible outcome of an infected patient for appropriate treatment using AI techniques. This paper proposes a fine-tuned Random Forest model boosted by the AdaBoost algorithm. The model uses the COVID-19 patient's geographical, travel, health, and demographic data to predict the severity of the case and the possible outcome, recovery, or death. The model has an accuracy of 94% and a F1 Score of 0.86 on the dataset used. The data analysis reveals a positive correlation between patients' gender and deaths, and also indicates that the majority of patients are aged between 20 and 70 years. Twit Text FOAVip COVID-19 Patient Health Prediction Using Boosted Random Forest Algorithm ????Front Public Health http://www.kidney.de/pget.php?&tw=1&pmid=32719767 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32719767 #FOAvip English Wikipedia <ref>{{Cite pmid|32719767}}</ref> COVID-19 Patient Health Prediction Using Boosted Random Forest Algorithm #MMPMID32719767 Iwendi C; Bashir AK; Peshkar A; Sujatha R; Chatterjee JM; Pasupuleti S; Mishra R; Pillai S; Jo O Front Public Health 2020[]; 8 (ä): 357 PMID32719767show ga Integration of artificial intelligence (AI) techniques in wireless infrastructure, real-time collection, and processing of end-user devices is now in high demand. It is now superlative to use AI to detect and predict pandemics of a colossal nature. The Coronavirus disease 2019 (COVID-19) pandemic, which originated in Wuhan China, has had disastrous effects on the global community and has overburdened advanced healthcare systems throughout the world. Globally; over 4,063,525 confirmed cases and 282,244 deaths have been recorded as of 11th May 2020, according to the European Centre for Disease Prevention and Control agency. However, the current rapid and exponential rise in the number of patients has necessitated efficient and quick prediction of the possible outcome of an infected patient for appropriate treatment using AI techniques. This paper proposes a fine-tuned Random Forest model boosted by the AdaBoost algorithm. The model uses the COVID-19 patient's geographical, travel, health, and demographic data to predict the severity of the case and the possible outcome, recovery, or death. The model has an accuracy of 94% and a F1 Score of 0.86 on the dataset used. The data analysis reveals a positive correlation between patients' gender and deaths, and also indicates that the majority of patients are aged between 20 and 70 years. |*Artificial Intelligence[MESH] |*Pandemics[MESH] |Adult[MESH] |Aged[MESH] |Algorithms[MESH] |COVID-19/*epidemiology[MESH] |China/epidemiology[MESH] |Female[MESH] |Humans[MESH] |Male[MESH] |Middle Aged[MESH]COVID-19 Patient Health Prediction Using Boosted Random Forest Algorit(epmc).pdf DeepDyve Pubget Overpricing