{"id":"W4393047893","doi":"10.17148/ijarcce.2024.13315","title":"Heart Disease Prediction System Using Machine Learning","year":2024,"lang":"en","type":"article","venue":"IJARCCE","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Artificial intelligence; Machine learning; Disease; 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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006842306,0.0001325396,0.0001662869,0.0001332444,0.001048563,0.00002874767,0.0001026515,0.0001295176,0.0003652135],"category_scores_gemma":[0.0002731698,0.0001210299,0.00006436076,0.0003159466,0.00003922957,0.0001895585,0.00008487301,0.0009685911,0.001814108],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005338003,"about_ca_system_score_gemma":0.0004205382,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003340307,"about_ca_topic_score_gemma":0.0001808018,"domain_scores_codex":[0.9977894,0.0006136405,0.0005078629,0.0003456403,0.0002734718,0.0004699196],"domain_scores_gemma":[0.9988499,0.0004505454,0.00006030489,0.0002443467,0.000119199,0.0002756869],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001568921,0.00003524591,0.9534525,0.00761641,0.00003382477,0.000187609,0.006300923,0.004426979,0.00213317,0.01832526,0.004272672,0.003058515],"study_design_scores_gemma":[0.00004348602,0.00004147347,0.004236939,0.002611932,0.00003863689,0.000008756494,0.003286511,0.9170042,0.0000828124,0.0004501852,0.07204748,0.0001476236],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9680352,0.006461388,0.005795061,0.004462758,0.007997326,0.00146975,0.0001901024,0.002548743,0.003039708],"genre_scores_gemma":[0.9963343,0.00001779679,0.0002445675,0.0003231067,0.001224615,0.0000779991,0.0000300911,0.00005237936,0.001695191],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9492156,"threshold_uncertainty_score":0.9989631,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1979801443706748,"score_gpt":0.4988218898224244,"score_spread":0.3008417454517496,"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."}}