{"id":"W2412875092","doi":"10.3233/978-1-60750-949-3-736","title":"Comparison of Machine Learning Techniques with Classical Statistical Models in Predicting Health Outcomes","year":2004,"lang":"en","type":"article","venue":"Studies in health technology and informatics","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":110,"is_retracted":false,"has_abstract":true,"ca_institutions":"Health Sciences Centre","funders":"","keywords":"Logistic regression; Perceptron; Multilayer perceptron; Machine learning; Artificial intelligence; Support vector machine; Artificial neural network; Computer science; Sample (material); Population; Data mining; Medicine; Environmental health","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":[0.0126942,0.001344978,0.001003373,0.003849007,0.0002879468,0.001142908,0.001206192,0.001122469,0.0008013085],"category_scores_gemma":[0.02976651,0.0002816211,0.001063736,0.003048744,0.0004538413,0.002401877,0.001113923,0.001119032,0.0004519644],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006262834,"about_ca_system_score_gemma":0.0006858211,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003032505,"about_ca_topic_score_gemma":0.002831955,"domain_scores_codex":[0.9932648,0.004183198,0.0004211746,0.0005170735,0.001463894,0.0001499316],"domain_scores_gemma":[0.9592285,0.03658963,0.0007960192,0.001005155,0.002167058,0.0002136617],"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":[0.00205818,0.0007722839,0.03928958,0.001080875,0.00181333,0.0001614535,0.0002416957,0.3489683,0.001349787,0.005626335,0.002771747,0.5958664],"study_design_scores_gemma":[0.00006740251,0.0004893707,0.005103466,0.00005034297,0.00009172547,0.00005706303,0.00006766109,0.9887085,0.0005198238,0.004253838,0.000570624,0.00002023402],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3528895,0.01624599,0.6184992,0.002325172,0.0005711937,0.0003050087,0.000768107,0.002004921,0.006390837],"genre_scores_gemma":[0.7583917,0.005122787,0.2333222,0.0003443755,0.0004165132,0.0001888334,0.000729696,0.0001020273,0.001381857],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0126942,"threshold_uncertainty_score":0.06713414,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2178134437015855,"score_gpt":0.546464264265972,"score_spread":0.3286508205643866,"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."}}