{"id":"W4293420779","doi":"10.3390/a15090308","title":"Early Prediction of Chronic Kidney Disease: A Comprehensive Performance Analysis of Deep Learning Models","year":2022,"lang":"en","type":"article","venue":"Algorithms","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan; Lakehead University","funders":"","keywords":"Kidney disease; Computer science; Survivability; Artificial intelligence; Artificial neural network; Predictive modelling; Machine learning; Binary classification; Deep learning; F1 score; Medicine; Internal medicine; Support vector machine","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":[0.003201952,0.001237276,0.0008162537,0.001163611,0.0003007981,0.0008891574,0.0005825775,0.0008213718,0.000742453],"category_scores_gemma":[0.004572401,0.0002113341,0.0007105918,0.0007263515,0.0001946863,0.001060368,0.000684305,0.0008646832,0.0002508723],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007975693,"about_ca_system_score_gemma":0.001072979,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01541507,"about_ca_topic_score_gemma":0.01402289,"domain_scores_codex":[0.9992995,0.0001794899,0.00007557147,0.0001385853,0.0001898637,0.0001169383],"domain_scores_gemma":[0.99843,0.0007890344,0.0001238742,0.0001404868,0.0004420336,0.00007453998],"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":[0.001037663,0.0006059555,0.1036532,0.000322869,0.000840953,0.0002469217,0.00008772998,0.6228641,0.00296956,0.001205273,0.007766459,0.2583993],"study_design_scores_gemma":[0.00001199439,0.0002875591,0.01274528,0.00005768165,0.0001018316,0.00005949161,0.00003911361,0.9835355,0.001627042,0.0007703035,0.0007456371,0.00001849048],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8874831,0.01090159,0.08932646,0.001660528,0.0002952535,0.00010707,0.003008356,0.001578951,0.005638697],"genre_scores_gemma":[0.9820238,0.001153521,0.01211848,0.0001224972,0.00004495534,0.00003849109,0.003171545,0.00003991348,0.001286759],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01541507,"threshold_uncertainty_score":0.03065068,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1056903421618994,"score_gpt":0.3895465118937039,"score_spread":0.2838561697318046,"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."}}