{"id":"W3102959652","doi":"10.1063/5.0030332","title":"Comparison of random forest and support vector machine for prediction of cognitive impairment in Parkinson's disease","year":2020,"lang":"en","type":"article","venue":"AIP conference proceedings","topic":"Parkinson's Disease Mechanisms and Treatments","field":"Medicine","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Support vector machine; Random forest; Cognition; Mean squared error; Montreal Cognitive Assessment; Dementia; Neuroimaging; Artificial intelligence; Cognitive impairment; Psychology; Audiology; Pattern recognition (psychology); Computer science; Statistics; Machine learning; Medicine; Mathematics; Neuroscience; Disease; Internal medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007973136,0.00145293,0.00149282,0.002239288,0.0003419617,0.000802168,0.0006602823,0.001111931,0.0007588805],"category_scores_gemma":[0.01074392,0.0002379055,0.001234222,0.00103291,0.0002274728,0.001175469,0.0003636107,0.0008119858,0.0003937011],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000471488,"about_ca_system_score_gemma":0.001118035,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0102054,"about_ca_topic_score_gemma":0.006132899,"domain_scores_codex":[0.9975947,0.001270071,0.0001773471,0.0003443385,0.00037854,0.0002350397],"domain_scores_gemma":[0.9922341,0.005697458,0.0003017622,0.000265645,0.001287134,0.0002138593],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003822679,0.000735216,0.05728482,0.0005116608,0.0007798677,0.0001921884,0.0001495937,0.4689008,0.002092479,0.001037281,0.00498871,0.4595047],"study_design_scores_gemma":[0.00005119215,0.0005290261,0.008538826,0.00004632427,0.0001028364,0.00005018257,0.00005876636,0.9891649,0.0006409858,0.0004690632,0.0003228475,0.00002500089],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7813112,0.01522351,0.1937526,0.001128066,0.0009320526,0.0002664465,0.001432579,0.0025134,0.003440217],"genre_scores_gemma":[0.9535705,0.001480284,0.0424683,0.0001121587,0.0001474094,0.0001070055,0.001320676,0.00005100795,0.0007424749],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0102054,"threshold_uncertainty_score":0.04216647,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03643786988341878,"score_gpt":0.2961425337011104,"score_spread":0.2597046638176916,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}