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10.1371/journal.pone.0155119 http://scihub22266oqcxt.onion/10.1371/journal.pone.0155119 C4873242!4873242!27195952     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 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\27195952.jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       PLoS+One 2016 ; 11 (5): � Nephropedia Template TP {{tp|p=27195952|t=2016. Multilevel Weighted Support Vector Machine for Classification on Healthcare Data with Missing Values |pdf=|usr=}}{{27195952}} gab.com Text Multilevel Weighted Support Vector Machine for Classification on Healthcare Data with Missing Values JO: PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=27195952 #FOAvip This work is motivated by the needs of predictive analytics on healthcare data as represented by Electronic Medical Records. Such data is invariably problematic: noisy, with missing entries, with imbalance in classes of interests, leading to serious bias in predictive modeling. Since standard data mining methods often produce poor performance measures, we argue for development of specialized techniques of data-preprocessing and classification. In this paper, we propose a new method to simultaneously classify large datasets and reduce the effects of missing values. It is based on a multilevel framework of the cost-sensitive SVM and the expected maximization imputation method for missing values, which relies on iterated regression analyses. We compare classification results of multilevel SVM-based algorithms on public benchmark datasets with imbalanced classes and missing values as well as real data in health applications, and show that our multilevel SVM-based method produces fast, and more accurate and robust classification results. Twit Text FOAVip Multilevel Weighted Support Vector Machine for Classification on Healthcare Data with Missing Values ????PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=27195952 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=27195952 #FOAvip English Wikipedia <ref>{{Cite pmid|27195952}}</ref> Multilevel Weighted Support Vector Machine for Classification on Healthcare Data with Missing Values #MMPMID27195952 Razzaghi T; Roderick O; Safro I; Marko N PLoS One 2016[]; 11 (5): � PMID27195952show ga This work is motivated by the needs of predictive analytics on healthcare data as represented by Electronic Medical Records. Such data is invariably problematic: noisy, with missing entries, with imbalance in classes of interests, leading to serious bias in predictive modeling. Since standard data mining methods often produce poor performance measures, we argue for development of specialized techniques of data-preprocessing and classification. In this paper, we propose a new method to simultaneously classify large datasets and reduce the effects of missing values. It is based on a multilevel framework of the cost-sensitive SVM and the expected maximization imputation method for missing values, which relies on iterated regression analyses. We compare classification results of multilevel SVM-based algorithms on public benchmark datasets with imbalanced classes and missing values as well as real data in health applications, and show that our multilevel SVM-based method produces fast, and more accurate and robust classification results. �Multilevel Weighted Support Vector Machine for Classification on Healt(epmc).pdf DeepDyve Pubget Overpricing