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10.1007/s42600-020-00112-5 http://scihub22266oqcxt.onion/10.1007/s42600-020-00112-5 C7790363/?report=reader!7790363!C7790363     free     free     free Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       ä-/-ä 2022 ; 38 (1): 99-116 Nephropedia Template TP {{tp|p=C7790363|t=2022. Heg IA: an intelligent system to support diagnosis of Covid-19 based on blood tests |pdf=|usr=}}{{C7790363}} gab.com Text Heg IA: an intelligent system to support diagnosis of Covid-19 based on blood tests JO: ä-/-ä http://www.kidney.de/pget.php?&tw=1&pmid=C7790363 #FOAvip Purpose: A new kind of coronavirus, the SARS-CoV-2, started the biggest pandemic of the century. More than a million people have been killed by Covid-19. Because of this, quick and precise diagnosis test is necessary. The current gold standard is the RT-PCR with DNA sequencing and identification, but its results take too long to be available. Tests base on IgM/IgG antibodies have been used, but their sensitivity and specificity may be very low. Many studies have been demonstrating the Covid-19 impact on hematological parameters. Method: This work proposes an intelligent system to support Covid-19 diagnosis based on blood testing. Laboratory parameters obtained from the hemogram and biochemical tests defined as standards to support clinical diagnosis were used as input features. Afterward, we used particle swarm optimization, evolutionary algorithms, and manual selection based on cost minimization to select the most significant features. Results: We tested several machine learning methods, and we achieved high classification performance: overall accuracy of 95.159%?±?0.693, kappa index of 0.903?±?0.014, sensitivity of 0.968?±?0.007, precision of 0.938?±?0.010, and specificity of 0.936?±?0.011. These results were achieved using classical and low computational cost classifiers, with Bayes Network being the best of them. In addition, only 24 blood tests were needed. Conclusion: This points to the possibility of a new rapid test with low cost. The desktop version of the system is fully functional and available for free use. Twit Text FOAVip Heg IA: an intelligent system to support diagnosis of Covid-19 based on blood tests ????ä-/-ä http://www.kidney.de/pget.php?&tw=1&pmid=C7790363 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=C7790363 #FOAvip English Wikipedia <ref>{{Cite pmid|C7790363}}</ref> Heg IA: an intelligent system to support diagnosis of Covid-19 based on blood tests #MMPMIDC7790363 de Freitas Barbosa VA; Gomes JC; de Santana MA; Albuquerque JEde A; de Souza RG; de Souza RE; dos Santos WP ä-/-ä 2022[]; 38 (1): 99-116 PMIDC7790363show ga Purpose: A new kind of coronavirus, the SARS-CoV-2, started the biggest pandemic of the century. More than a million people have been killed by Covid-19. Because of this, quick and precise diagnosis test is necessary. The current gold standard is the RT-PCR with DNA sequencing and identification, but its results take too long to be available. Tests base on IgM/IgG antibodies have been used, but their sensitivity and specificity may be very low. Many studies have been demonstrating the Covid-19 impact on hematological parameters. Method: This work proposes an intelligent system to support Covid-19 diagnosis based on blood testing. Laboratory parameters obtained from the hemogram and biochemical tests defined as standards to support clinical diagnosis were used as input features. Afterward, we used particle swarm optimization, evolutionary algorithms, and manual selection based on cost minimization to select the most significant features. Results: We tested several machine learning methods, and we achieved high classification performance: overall accuracy of 95.159%?±?0.693, kappa index of 0.903?±?0.014, sensitivity of 0.968?±?0.007, precision of 0.938?±?0.010, and specificity of 0.936?±?0.011. These results were achieved using classical and low computational cost classifiers, with Bayes Network being the best of them. In addition, only 24 blood tests were needed. Conclusion: This points to the possibility of a new rapid test with low cost. The desktop version of the system is fully functional and available for free use. äHeg IA: an intelligent system to support diagnosis of Covid-19 based o(epmc).pdf DeepDyve Pubget Overpricing