{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001107391,0.0007873095,0.001014628,0.002696178,0.0004858143,0.001354967,0.001123078,0.001016871,0.002312212],"category_scores_gemma":[0.003029759,0.0002194535,0.0009683828,0.001306976,0.000207493,0.0009842935,0.0004705267,0.0007859824,0.001240749],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007613027,"about_ca_system_score_gemma":0.0009891368,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00787617,"about_ca_topic_score_gemma":0.006537558,"domain_scores_codex":[0.9986854,0.000215701,0.000169581,0.000258001,0.0005249874,0.0001462892],"domain_scores_gemma":[0.9989618,0.0003204072,0.0001115924,0.00005407259,0.0004845046,0.00006760034],"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":[0.0005857142,0.0005382223,0.04338677,0.0004590402,0.0003871309,0.0009213533,0.0001949881,0.1543054,0.005643281,0.005673828,0.01547959,0.7724248],"study_design_scores_gemma":[0.00003845442,0.0002900225,0.01104954,0.000171464,0.0001428497,0.0007144123,0.0001308299,0.9668493,0.004968879,0.007975263,0.007595931,0.0000729546],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1290704,0.004768984,0.8402066,0.001711857,0.001037674,0.0006652753,0.002068335,0.003538564,0.01693224],"genre_scores_gemma":[0.7681986,0.002725975,0.216838,0.0003823592,0.0003771989,0.0002862318,0.002588929,0.00008530082,0.008517312],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00787617,"threshold_uncertainty_score":0.01566064,"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."}}