{"id":"W4393992991","doi":"10.3390/s24072312","title":"Advancing Breast Cancer Diagnosis through Breast Mass Images, Machine Learning, and Regression Models","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"AI in cancer detection","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre Hospitalier de l’Université de Montréal","funders":"","keywords":"Support vector machine; Artificial intelligence; Naive Bayes classifier; Machine learning; Decision tree; Breast cancer; Computer science; Classifier (UML); Cross-validation; Computer-aided diagnosis; Cancer; Pattern recognition (psychology); Medicine; Internal medicine","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":[],"consensus_categories":[],"category_scores_codex":[0.0001701791,0.0002086154,0.0001737413,0.00009782715,0.0001997734,0.0002419612,0.0002199767,0.00007936088,0.00007160673],"category_scores_gemma":[0.0000102263,0.0001732959,0.00005876699,0.0004238909,0.00005941434,0.001019585,0.0001777407,0.0003653584,0.00002103055],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002034821,"about_ca_system_score_gemma":0.00005341581,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00094483,"about_ca_topic_score_gemma":0.00007051322,"domain_scores_codex":[0.9984679,0.00009259098,0.0001820171,0.0006255565,0.0002928395,0.0003391094],"domain_scores_gemma":[0.999404,0.0001075015,0.00006438365,0.0002707267,0.00006018545,0.00009321942],"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":[0.00004966756,0.00004156956,0.01128488,0.0003494432,0.0001066992,0.0002502648,0.002930363,0.04065839,0.003921191,0.002923602,0.008463767,0.9290202],"study_design_scores_gemma":[0.0002422036,0.00005028037,0.001078759,0.0006509731,0.00002804852,0.0006732881,0.00008645662,0.9652232,0.004660531,0.005428138,0.02147895,0.0003991528],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1159682,0.04416143,0.7904112,0.0328264,0.005675998,0.0008118275,0.0004200794,0.003705387,0.006019421],"genre_scores_gemma":[0.9714885,0.007384697,0.0190875,0.0001602926,0.0003609956,0.00009497722,0.000003959088,0.0000534562,0.00136558],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.928621,"threshold_uncertainty_score":0.7066804,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009837236044524375,"score_gpt":0.2690494362093516,"score_spread":0.2592122001648272,"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."}}