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10.3389/fncom.2015.00137 http://scihub22266oqcxt.onion/10.3389/fncom.2015.00137 C4633498!4633498 !26594165     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=26594165 &cmd=llinks): Failed to open stream: HTTP request failed! 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Distinguishing Parkinson s disease from atypical parkinsonian syndromes using PET data and a computer system based on support vector machines and Bayesian networks |pdf=|usr=}}{{26594165 }} gab.com Text Distinguishing Parkinson s disease from atypical parkinsonian syndromes using PET data and a computer system based on support vector machines and Bayesian networks JO: Front Comput Neurosci http://www.kidney.de/pget.php?&tw=1&pmid=26594165 #FOAvip Differentiating between Parkinson's disease (PD) and atypical parkinsonian syndromes (APS) is still a challenge, specially at early stages when the patients show similar symptoms. During last years, several computer systems have been proposed in order to improve the diagnosis of PD, but their accuracy is still limited. In this work we demonstrate a full automatic computer system to assist the diagnosis of PD using (18)F-DMFP PET data. First, a few regions of interest are selected by means of a two-sample t-test. The accuracy of the selected regions to separate PD from APS patients is then computed using a support vector machine classifier. The accuracy values are finally used to train a Bayesian network that can be used to predict the class of new unseen data. This methodology was evaluated using a database with 87 neuroimages, achieving accuracy rates over 78%. A fair comparison with other similar approaches is also provided. Twit Text FOAVip Distinguishing Parkinson s disease from atypical parkinsonian syndromes using PET data and a computer system based on support vector machines and Bayesian networks ????Front Comput Neurosci http://www.kidney.de/pget.php?&tw=1&pmid=26594165 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26594165 #FOAvip English Wikipedia <ref>{{Cite pmid|26594165 }}</ref> Distinguishing Parkinson s disease from atypical parkinsonian syndromes using PET data and a computer system based on support vector machines and Bayesian networks #MMPMID26594165 Segovia F ; Illán IA ; Górriz JM ; Ramírez J ; Rominger A ; Levin J Front Comput Neurosci 2015[]; 9 (ä): 137 PMID26594165 show ga Differentiating between Parkinson's disease (PD) and atypical parkinsonian syndromes (APS) is still a challenge, specially at early stages when the patients show similar symptoms. During last years, several computer systems have been proposed in order to improve the diagnosis of PD, but their accuracy is still limited. In this work we demonstrate a full automatic computer system to assist the diagnosis of PD using (18)F-DMFP PET data. First, a few regions of interest are selected by means of a two-sample t-test. The accuracy of the selected regions to separate PD from APS patients is then computed using a support vector machine classifier. The accuracy values are finally used to train a Bayesian network that can be used to predict the class of new unseen data. This methodology was evaluated using a database with 87 neuroimages, achieving accuracy rates over 78%. A fair comparison with other similar approaches is also provided. äDistinguishing Parkinson s disease from atypical parkinsonian syndrome(epmc).pdf DeepDyve Pubget Overpricing