{"id":"W4220684949","doi":"10.18280/ria.360118","title":"Risk Assessment of Cardiovascular Diseases Using kNN and Decision Tree Classifier","year":2022,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Decision tree; Disease; Classifier (UML); Artificial intelligence; Machine learning; Computer science; Decision tree learning; Diabetes mellitus; Cardiovascular event; Medicine; Intensive care medicine; Internal medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002087949,0.0002626506,0.0006868863,0.0002788767,0.002402148,0.00001720763,0.0004486598,0.0001451673,0.002167304],"category_scores_gemma":[0.0008586472,0.0002668744,0.0003377932,0.0007720204,0.0002387983,0.0001740797,0.0006994934,0.001101036,0.00006152035],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004580456,"about_ca_system_score_gemma":0.0005372506,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002155169,"about_ca_topic_score_gemma":0.0001822018,"domain_scores_codex":[0.9952751,0.001177057,0.001431617,0.0007216108,0.0007160609,0.0006785608],"domain_scores_gemma":[0.995865,0.001783541,0.0005629938,0.001091817,0.0003719824,0.0003246733],"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":[0.0001681494,0.0003893763,0.2721742,0.0005251514,0.0002428824,0.00004472622,0.005127765,0.4234411,0.0007689389,0.008042187,0.0006780921,0.2883975],"study_design_scores_gemma":[0.00008886236,0.0002876144,0.004356379,0.0002580391,0.0002464165,0.00001297969,0.02763123,0.94,0.0007101927,0.01074658,0.01528551,0.0003762241],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8648008,0.003904631,0.1258002,0.0002172022,0.001596421,0.001480858,0.0002236916,0.00009876212,0.001877471],"genre_scores_gemma":[0.9941141,0.0008500044,0.004125211,0.0001387217,0.0002115292,0.0002247001,0.00001679065,0.00005366817,0.0002653014],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5165589,"threshold_uncertainty_score":0.9999784,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1578716135115671,"score_gpt":0.4489916933192806,"score_spread":0.2911200798077134,"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."}}