{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001297504,0.0006106488,0.0008799889,0.001554317,0.0003313437,0.0005636616,0.0004945167,0.0005571643,0.0007055391],"category_scores_gemma":[0.003016724,0.0001977289,0.0007221238,0.001129093,0.0002555538,0.0003837477,0.0002762664,0.0005195195,0.0002274158],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004860059,"about_ca_system_score_gemma":0.001035717,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005072653,"about_ca_topic_score_gemma":0.003714154,"domain_scores_codex":[0.9994934,0.0002215553,0.00003105654,0.00007583777,0.0001192346,0.0000589017],"domain_scores_gemma":[0.9992452,0.0004845799,0.00005799988,0.00004049538,0.0001548564,0.00001694418],"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":[0.0002557362,0.0002005217,0.01010204,0.0001143772,0.0001751196,0.0002062945,0.00008121922,0.5254776,0.01620463,0.002382107,0.002216156,0.4425841],"study_design_scores_gemma":[0.00001853183,0.00008342779,0.002116519,0.00001227721,0.0000363572,0.00008248894,0.00001831592,0.9925359,0.003056615,0.0014397,0.0005890972,0.00001075778],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1563009,0.0008735077,0.8392736,0.000320959,0.00006244051,0.0001604791,0.0002106734,0.001352272,0.001445073],"genre_scores_gemma":[0.7276127,0.0002718046,0.2706095,0.00009406017,0.00002752482,0.0001838718,0.0004180073,0.00005940028,0.0007230065],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005072653,"threshold_uncertainty_score":0.01008624,"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."}}