{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001106743,0.000830441,0.001120467,0.0007940529,0.00043524,0.0007172815,0.001161652,0.0008625852,0.001394484],"category_scores_gemma":[0.001560497,0.0002736418,0.0009879327,0.0006561705,0.0001973018,0.0007173294,0.0005627853,0.001275611,0.0005332085],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000692041,"about_ca_system_score_gemma":0.001031014,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01367788,"about_ca_topic_score_gemma":0.01298862,"domain_scores_codex":[0.9996464,0.00009769235,0.00002257076,0.00009244828,0.0000718688,0.00006902261],"domain_scores_gemma":[0.9994212,0.0002705485,0.0000357649,0.0000347918,0.0002101567,0.00002751867],"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":[0.0001776579,0.0001919224,0.007175284,0.00005750434,0.0001252832,0.0001054815,0.00006526886,0.773029,0.00172748,0.002071511,0.00461945,0.2106542],"study_design_scores_gemma":[0.000002132994,0.00001819743,0.0003264425,0.000003566204,0.00001075352,0.000008970542,0.00000512503,0.9986557,0.0002051628,0.0005464924,0.0002143214,0.000003085442],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1943414,0.003427834,0.7927087,0.001280405,0.0002956429,0.0001248943,0.0008221138,0.001857194,0.005141871],"genre_scores_gemma":[0.9125243,0.000933737,0.07819214,0.0002962005,0.0001583573,0.0001881598,0.001502905,0.0000557592,0.0061483],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01367788,"threshold_uncertainty_score":0.02719653,"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."}}