{"id":"W2944714746","doi":"10.1109/isivc.2018.8709214","title":"Breast Cancer Diagnosis using Quality Control Charts and Logistic Regression","year":2018,"lang":"en","type":"article","venue":"","topic":"AI in cancer detection","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Artificial intelligence; Visualization; Dimensionality reduction; Logistic regression; Confusion matrix; Receiver operating characteristic; Principal component analysis; Outlier; Machine learning; Breast cancer; Data mining; Pattern recognition (psychology); Cancer; 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":[0.0002458986,0.00009312588,0.0001229519,0.00004160458,0.0001783138,0.00008929663,0.0001879158,0.00004930009,0.0001343373],"category_scores_gemma":[0.00002365388,0.00007073425,0.00002040883,0.0001569979,0.000123592,0.0003412558,0.0001081299,0.00005804886,0.00001111196],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001062644,"about_ca_system_score_gemma":0.000043994,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001721653,"about_ca_topic_score_gemma":0.0002020144,"domain_scores_codex":[0.9991042,0.00007913173,0.0001455355,0.0003245665,0.0001688599,0.0001776908],"domain_scores_gemma":[0.9993224,0.0001045058,0.00008946996,0.0002962873,0.0001143278,0.00007302882],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0001081578,0.00009679543,0.219691,0.00007274835,0.00005876603,0.000009211089,0.000878142,0.00007890112,0.006322612,0.01070359,0.006084453,0.7558956],"study_design_scores_gemma":[0.002194196,0.0002527488,0.4864729,0.0003540537,0.00004616911,0.0002092296,0.00005361786,0.4799086,0.02081488,0.006127724,0.002757966,0.0008079127],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1428938,0.0002320676,0.852365,0.002501619,0.001208281,0.0001622756,0.00001435333,0.0001856954,0.0004369938],"genre_scores_gemma":[0.9946495,0.0000469568,0.004330862,0.0005494973,0.0003176083,0.00003993078,1.014164e-7,0.000005167328,0.00006034291],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8517557,"threshold_uncertainty_score":0.2884459,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07999619502835428,"score_gpt":0.367593625150769,"score_spread":0.2875974301224148,"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."}}