{"id":"W3094177185","doi":"10.1109/icccnt49239.2020.9225548","title":"Risk Prediction Of Chronic Kidney Disease Using Machine Learning Algorithms","year":2020,"lang":"en","type":"article","venue":"","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":73,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Random forest; Python (programming language); Kidney disease; Machine learning; Computer science; Demise; Algorithm; Artificial intelligence; Data set; Medicine; Internal medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001604382,0.0006927701,0.0008419541,0.00229273,0.0003840438,0.0009159527,0.0005847678,0.0006673021,0.001241651],"category_scores_gemma":[0.005472196,0.0002384801,0.0008652564,0.001244563,0.0001385306,0.000670486,0.0003939478,0.0009454433,0.0005678486],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005222694,"about_ca_system_score_gemma":0.0008557918,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008883279,"about_ca_topic_score_gemma":0.006070584,"domain_scores_codex":[0.9991598,0.0002309715,0.0001218235,0.0002157343,0.0001942258,0.00007735736],"domain_scores_gemma":[0.9975367,0.001589155,0.0002432453,0.0001004818,0.0004583284,0.00007214785],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003276295,0.0006597956,0.1279294,0.0001765557,0.0003336851,0.0003741222,0.00009955459,0.4231918,0.002572419,0.00153672,0.009847092,0.4329512],"study_design_scores_gemma":[0.000008031763,0.00003974569,0.006458204,0.00002443855,0.00001893357,0.00007364377,0.00001784945,0.9900664,0.0008865984,0.001707129,0.0006839811,0.000015037],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3564653,0.003159004,0.6225663,0.001791123,0.0003664645,0.0002838244,0.003857452,0.006668618,0.004842003],"genre_scores_gemma":[0.8085281,0.000687102,0.1845566,0.0002190692,0.0001809481,0.0001658294,0.003540845,0.00006530709,0.002056196],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008883279,"threshold_uncertainty_score":0.01766312,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1694537069559504,"score_gpt":0.4485674794241015,"score_spread":0.2791137724681512,"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."}}