{"id":"W2907274433","doi":"10.2174/2213275912666190101121058","title":"Importance of Feature Selection and Data Visualization Towards Prediction of Breast Cancer","year":2019,"lang":"en","type":"article","venue":"Recent Patents on Computer Science","topic":"AI in cancer detection","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Machine learning; Random forest; Feature selection; Artificial intelligence; Support vector machine; Breast cancer; Multilayer perceptron; Artificial neural network; Classifier (UML); Visualization; Perceptron; Principal component analysis; Data mining; Pattern recognition (psychology); Cancer; Medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001827691,0.0008998553,0.0005803063,0.002187328,0.0003111485,0.001756365,0.0005239397,0.0005749064,0.002153467],"category_scores_gemma":[0.01058482,0.0001478277,0.000668187,0.001884571,0.0002103479,0.001023186,0.0004569401,0.0009498527,0.0006790241],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003317315,"about_ca_system_score_gemma":0.0004543636,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002225913,"about_ca_topic_score_gemma":0.001637417,"domain_scores_codex":[0.9988181,0.0004705906,0.0001005809,0.0002021094,0.0003306727,0.00007791386],"domain_scores_gemma":[0.9919865,0.005615635,0.0004169271,0.0003998498,0.001420086,0.0001609756],"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":[0.001339837,0.0004584061,0.08460917,0.001104663,0.0002032902,0.0006914594,0.0005392414,0.05866273,0.02645116,0.001870136,0.0261336,0.7979364],"study_design_scores_gemma":[0.000126144,0.001211984,0.1270323,0.0004502949,0.0003040897,0.001895519,0.001144434,0.7433911,0.08093927,0.01045898,0.0328366,0.0002091822],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.591838,0.007763672,0.3578918,0.004754213,0.0009616042,0.0004890212,0.009496895,0.01633862,0.01046625],"genre_scores_gemma":[0.8369541,0.001164471,0.1565185,0.0001019631,0.0001333476,0.0001465313,0.003248772,0.0002699866,0.001462312],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002225913,"threshold_uncertainty_score":0.009665847,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03270891608972389,"score_gpt":0.2985276057406025,"score_spread":0.2658186896508786,"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."}}