{"id":"W4307703018","doi":"10.3390/su142113998","title":"Optimized Stacking Ensemble Learning Model for Breast Cancer Detection and Classification Using Machine Learning","year":2022,"lang":"en","type":"article","venue":"Sustainability","topic":"AI in cancer detection","field":"Computer Science","cited_by":110,"is_retracted":false,"has_abstract":true,"ca_institutions":"Brandon University","funders":"","keywords":"Machine learning; Artificial intelligence; Breast cancer; Categorization; Computer science; Boosting (machine learning); Ensemble learning; Classifier (UML); Overfitting; Gradient boosting; Health care; Random forest; Medicine; Cancer; Internal medicine; 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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.001247483,0.0001464786,0.0001723936,0.0001374196,0.001546567,0.0001298493,0.0002238611,0.00005141521,0.00001038844],"category_scores_gemma":[0.0002200444,0.0001705363,0.00006483384,0.0004313673,0.00004905746,0.0005542798,0.0003618437,0.000469734,1.133783e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003275718,"about_ca_system_score_gemma":0.0004114972,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005581917,"about_ca_topic_score_gemma":0.00005688707,"domain_scores_codex":[0.9982135,0.000326104,0.0002471955,0.000608124,0.0002620385,0.0003430229],"domain_scores_gemma":[0.9988427,0.000126593,0.0002178931,0.0002791762,0.0004706993,0.0000630098],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001483522,0.00002206821,0.003357753,0.00008502101,0.000009620597,5.151439e-7,0.001133731,0.8619848,0.004521859,0.0003949261,0.000001304488,0.1283401],"study_design_scores_gemma":[0.0005414645,0.00008733408,0.002250013,0.000003883065,0.00001581493,0.00002208638,0.0006282741,0.9899152,0.0005097406,0.005583734,0.000261505,0.0001809433],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1976019,0.0001084967,0.8008216,0.0005697616,0.0001596283,0.0005122212,0.000006914834,0.0002086122,0.00001082602],"genre_scores_gemma":[0.9819046,0.00001275774,0.01743859,0.00002883265,0.00004580466,0.0003538104,0.000002560885,0.00002133841,0.000191693],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7843027,"threshold_uncertainty_score":0.9997533,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02880260261634244,"score_gpt":0.2964242684119681,"score_spread":0.2676216657956257,"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."}}