{"id":"W4387422232","doi":"10.3390/technologies11050138","title":"Heuristic Weight Initialization for Diagnosing Heart Diseases Using Feature Ranking","year":2023,"lang":"en","type":"article","venue":"Technologies","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Royal Military College of Canada","funders":"National Research Foundation of Korea; National Research Foundation","keywords":"Computer science; Initialization; Artificial intelligence; Heuristic; Machine learning; Classifier (UML); Feature (linguistics); Feature engineering; Deep learning","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.0003198092,0.000161528,0.000269448,0.0003426266,0.00157461,0.00001961418,0.0002370678,0.0004224666,0.00001682815],"category_scores_gemma":[0.004937162,0.0001482833,0.00006832163,0.0008920434,0.0001501331,0.0001515995,0.0002046181,0.0004253547,0.000138904],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000189559,"about_ca_system_score_gemma":0.0001729001,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001510699,"about_ca_topic_score_gemma":0.0001252339,"domain_scores_codex":[0.9983094,0.0001275428,0.0003949175,0.0003278821,0.0001814477,0.000658861],"domain_scores_gemma":[0.9972779,0.001924924,0.0001639237,0.0003618435,0.0002305247,0.00004091143],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001460295,0.00006803391,0.7712179,0.002385579,0.00004275396,0.00004050347,0.003042748,0.0007933963,0.002858949,0.03563542,0.1522159,0.0315528],"study_design_scores_gemma":[0.001150533,0.0004652474,0.03280172,0.006805191,0.0003032342,0.00001316369,0.1039376,0.2965553,0.02150358,0.3766655,0.1578108,0.0019881],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9130485,0.002830121,0.01840626,0.02800173,0.004018455,0.004397854,0.0002812197,0.02878613,0.0002297139],"genre_scores_gemma":[0.9959316,0.0002723891,0.00236728,0.0003552495,0.0003090723,0.0005565503,0.00007831184,0.00005050674,0.00007905873],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7384162,"threshold_uncertainty_score":0.9997252,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2371329315287754,"score_gpt":0.5158390178956147,"score_spread":0.2787060863668394,"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."}}