{"id":"W4318969257","doi":"10.1109/icdsaai55433.2022.10028959","title":"Excogitation of Stacked Strategy for Coronary Artery Disease Diagnosis","year":2022,"lang":"en","type":"article","venue":"","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"CAD; Computer science; Artificial intelligence; Boosting (machine learning); Classifier (UML); Machine learning; Coronary artery disease; Support vector machine; Feature extraction; Extreme learning machine; Statistical classification; Stacking; Pattern recognition (psychology); Medicine; Cardiology; Engineering; Artificial neural network","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":[0.0008977297,0.000882421,0.0008714927,0.00136246,0.0006681787,0.001287393,0.0008724785,0.000891435,0.004314473],"category_scores_gemma":[0.001899965,0.000264852,0.001053192,0.0006865947,0.0003637698,0.0008495467,0.001152732,0.0005706109,0.001817506],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003234957,"about_ca_system_score_gemma":0.001028321,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003241203,"about_ca_topic_score_gemma":0.002926266,"domain_scores_codex":[0.999254,0.0001634977,0.00006052729,0.0001744956,0.0002417315,0.0001056727],"domain_scores_gemma":[0.9994523,0.0001163889,0.00003823894,0.00009091561,0.000236427,0.00006579311],"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":[0.0005218581,0.000336049,0.01288427,0.0001678569,0.0002812678,0.001180943,0.0002299017,0.05982801,0.01978986,0.01128139,0.01138171,0.8821169],"study_design_scores_gemma":[0.0000539327,0.0004070736,0.00562635,0.00005247496,0.0002456514,0.001418898,0.0001714156,0.9461552,0.01864944,0.01672425,0.01043507,0.00006033752],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07568366,0.001871755,0.9086645,0.000893387,0.000577218,0.0001960281,0.0004020529,0.00271554,0.008995809],"genre_scores_gemma":[0.627077,0.001149781,0.3617119,0.0005737753,0.0004624144,0.0001625107,0.001394248,0.0002583881,0.007209886],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004314473,"threshold_uncertainty_score":0.01443338,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3193880478090794,"score_gpt":0.5123206296233122,"score_spread":0.1929325818142327,"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."}}