{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001510088,0.0009136351,0.0009318485,0.002000015,0.0002106746,0.001228907,0.0009113185,0.000824446,0.001169123],"category_scores_gemma":[0.004565362,0.0004320409,0.0008512341,0.001621118,0.0002834771,0.001508532,0.000439308,0.0009788871,0.0008696528],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007156323,"about_ca_system_score_gemma":0.0005934906,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007815083,"about_ca_topic_score_gemma":0.006333665,"domain_scores_codex":[0.9993336,0.0001800954,0.00004303149,0.0001609774,0.0002239175,0.00005837137],"domain_scores_gemma":[0.9988074,0.0005902685,0.0001572358,0.00008212236,0.00033245,0.00003046847],"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":[0.0002249656,0.0002730102,0.01850806,0.0004370674,0.0002296917,0.0002380084,0.000113846,0.3935646,0.00995821,0.005166232,0.007325905,0.5639605],"study_design_scores_gemma":[0.000004617745,0.00002745606,0.001634954,0.00002027625,0.00002997418,0.00006545932,0.00002042452,0.9931404,0.0017503,0.001496204,0.001793696,0.00001622186],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06715766,0.00883868,0.9163249,0.001266283,0.0002177578,0.0001630217,0.0005468497,0.002548676,0.002936186],"genre_scores_gemma":[0.5614727,0.00683903,0.4246719,0.0003374578,0.0003594752,0.0001599082,0.001187273,0.0001636797,0.004808652],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007815083,"threshold_uncertainty_score":0.01553923,"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."}}