ML results from complex network measures.
<p>(A) The confusion matrix indicates that there were a lot of incorrect predictions between the TD and ADHD groups. (B) The ROC curve, where the dashed pink line represents the random choice classifier, the purple line is the micro-average ROC curve, the gray line is the macro-average ROC cur...
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| مؤلفون آخرون: | , , , , , , , |
| منشور في: |
2024
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| _version_ | 1852025874444976128 |
|---|---|
| author | Caroline L. Alves (14271413) |
| author2 | Tiago Martinelli (10402035) Loriz Francisco Sallum (19865127) Francisco Aparecido Rodrigues (19865130) Thaise G. L. de O. Toutain (19865133) Joel Augusto Moura Porto (19865136) Christiane Thielemann (14271419) Patrícia Maria de Carvalho Aguiar (19865139) Michael Moeckel (19865142) |
| author2_role | author author author author author author author author |
| author_facet | Caroline L. Alves (14271413) Tiago Martinelli (10402035) Loriz Francisco Sallum (19865127) Francisco Aparecido Rodrigues (19865130) Thaise G. L. de O. Toutain (19865133) Joel Augusto Moura Porto (19865136) Christiane Thielemann (14271419) Patrícia Maria de Carvalho Aguiar (19865139) Michael Moeckel (19865142) |
| author_role | author |
| dc.creator.none.fl_str_mv | Caroline L. Alves (14271413) Tiago Martinelli (10402035) Loriz Francisco Sallum (19865127) Francisco Aparecido Rodrigues (19865130) Thaise G. L. de O. Toutain (19865133) Joel Augusto Moura Porto (19865136) Christiane Thielemann (14271419) Patrícia Maria de Carvalho Aguiar (19865139) Michael Moeckel (19865142) |
| dc.date.none.fl_str_mv | 2024-10-17T17:35:10Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0305630.g010 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/ML_results_from_complex_network_measures_/27251103 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Medicine Cell Biology Neuroscience Biotechnology Science Policy Mental Health Biological Sciences not elsewhere classified totaling 120 subjects targeted intervention difficult surpassing existing benchmarks observed connectivity patterns brain network integration autism spectrum disorder achieve superior accuracy brain regions critical leveraging multiclass classification asd show disruptions ml classification rests multiclass classification regions involved typically developed three groups segregation among promising avenue overlapping symptoms impulse control findings pave established practices enhanced diagnostics cognitive functions clinical symptoms accurate diagnosis |
| dc.title.none.fl_str_mv | ML results from complex network measures. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <p>(A) The confusion matrix indicates that there were a lot of incorrect predictions between the TD and ADHD groups. (B) The ROC curve, where the dashed pink line represents the random choice classifier, the purple line is the micro-average ROC curve, the gray line is the macro-average ROC curve, the turquoise line the ROC curve referring to the TD class, the orange line the ROC curve referring to the ADHD class (which can be seen the ADHD has the lowest-distinguished curve) and the green line the ROC curve referring to the ASD class (which can be seen the ASD has the best-distinguished curve).</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_cba2caa38ae4969792ce0be6d5ebd8c2 |
| identifier_str_mv | 10.1371/journal.pone.0305630.g010 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/27251103 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | ML results from complex network measures.Caroline L. Alves (14271413)Tiago Martinelli (10402035)Loriz Francisco Sallum (19865127)Francisco Aparecido Rodrigues (19865130)Thaise G. L. de O. Toutain (19865133)Joel Augusto Moura Porto (19865136)Christiane Thielemann (14271419)Patrícia Maria de Carvalho Aguiar (19865139)Michael Moeckel (19865142)MedicineCell BiologyNeuroscienceBiotechnologyScience PolicyMental HealthBiological Sciences not elsewhere classifiedtotaling 120 subjectstargeted intervention difficultsurpassing existing benchmarksobserved connectivity patternsbrain network integrationautism spectrum disorderachieve superior accuracybrain regions criticalleveraging multiclass classificationasd show disruptionsml classification restsmulticlass classificationregions involvedtypically developedthree groupssegregation amongpromising avenueoverlapping symptomsimpulse controlfindings paveestablished practicesenhanced diagnosticscognitive functionsclinical symptomsaccurate diagnosis<p>(A) The confusion matrix indicates that there were a lot of incorrect predictions between the TD and ADHD groups. (B) The ROC curve, where the dashed pink line represents the random choice classifier, the purple line is the micro-average ROC curve, the gray line is the macro-average ROC curve, the turquoise line the ROC curve referring to the TD class, the orange line the ROC curve referring to the ADHD class (which can be seen the ADHD has the lowest-distinguished curve) and the green line the ROC curve referring to the ASD class (which can be seen the ASD has the best-distinguished curve).</p>2024-10-17T17:35:10ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0305630.g010https://figshare.com/articles/figure/ML_results_from_complex_network_measures_/27251103CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/272511032024-10-17T17:35:10Z |
| spellingShingle | ML results from complex network measures. Caroline L. Alves (14271413) Medicine Cell Biology Neuroscience Biotechnology Science Policy Mental Health Biological Sciences not elsewhere classified totaling 120 subjects targeted intervention difficult surpassing existing benchmarks observed connectivity patterns brain network integration autism spectrum disorder achieve superior accuracy brain regions critical leveraging multiclass classification asd show disruptions ml classification rests multiclass classification regions involved typically developed three groups segregation among promising avenue overlapping symptoms impulse control findings pave established practices enhanced diagnostics cognitive functions clinical symptoms accurate diagnosis |
| status_str | publishedVersion |
| title | ML results from complex network measures. |
| title_full | ML results from complex network measures. |
| title_fullStr | ML results from complex network measures. |
| title_full_unstemmed | ML results from complex network measures. |
| title_short | ML results from complex network measures. |
| title_sort | ML results from complex network measures. |
| topic | Medicine Cell Biology Neuroscience Biotechnology Science Policy Mental Health Biological Sciences not elsewhere classified totaling 120 subjects targeted intervention difficult surpassing existing benchmarks observed connectivity patterns brain network integration autism spectrum disorder achieve superior accuracy brain regions critical leveraging multiclass classification asd show disruptions ml classification rests multiclass classification regions involved typically developed three groups segregation among promising avenue overlapping symptoms impulse control findings pave established practices enhanced diagnostics cognitive functions clinical symptoms accurate diagnosis |