{"id":"W4389544981","doi":"10.1109/icit59216.2023.10335827","title":"Genetic Algorithm-Based Feature Selection for Accurate Breast Cancer Classification","year":2023,"lang":"en","type":"article","venue":"","topic":"AI in cancer detection","field":"Computer Science","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"","keywords":"Random forest; Feature selection; Computer science; Machine learning; Artificial intelligence; Breast cancer; Feature (linguistics); Binary classification; Genetic algorithm; Selection (genetic algorithm); Precision and recall; Recall; Pattern recognition (psychology); Data mining; Cancer; Support vector machine; 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":[0.0001339001,0.0001079973,0.00008193687,0.0001485558,0.0001762111,0.0001254917,0.000303706,0.00009088707,0.0000231279],"category_scores_gemma":[0.00000750226,0.0001006229,0.00005274353,0.00119155,0.00001559437,0.0002830899,0.00003046959,0.00008660201,0.00005520427],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002194196,"about_ca_system_score_gemma":0.0001660526,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000101065,"about_ca_topic_score_gemma":0.00009457728,"domain_scores_codex":[0.9990209,0.00002901441,0.0001191914,0.0003999661,0.0001865393,0.0002444029],"domain_scores_gemma":[0.9993632,0.0000576987,0.00007900208,0.0002512666,0.0001959789,0.00005282535],"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":[0.00001606021,0.00001858882,0.0009091599,0.00002905305,0.00001781369,4.955469e-7,0.00005309354,0.009535336,0.006625082,0.000712422,0.04543981,0.9366431],"study_design_scores_gemma":[0.0002529472,0.00003973889,0.07276125,0.000007945498,0.000006322893,0.000007125548,0.000008187403,0.91504,0.004806456,0.0005303239,0.006413905,0.0001257496],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001339212,0.0000216633,0.9889237,0.007507129,0.0008842644,0.0003509932,0.00001967414,0.0008504223,0.0001029492],"genre_scores_gemma":[0.4909488,0.0001014556,0.4984632,0.001481821,0.001331869,0.001894786,0.00003270211,0.00006335091,0.005682005],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9365174,"threshold_uncertainty_score":0.4103281,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02950919695893974,"score_gpt":0.2971635982718126,"score_spread":0.2676544013128729,"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."}}