{"id":"W2786454739","doi":"","title":"Performance Comparison of Machine Learning Techniques for Breast Cancer Detection","year":2018,"lang":"en","type":"article","venue":"Nova Journal of Engineering and Applied Sciences","topic":"AI in cancer detection","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"C4.5 algorithm; Machine learning; AdaBoost; Artificial intelligence; Breast cancer; Decision tree; Support vector machine; Logistic regression; Naive Bayes classifier; Computer science; Cancer; Statistical classification; Algorithm; Medicine; Internal medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.003268089,0.0007404136,0.001014532,0.002671124,0.000346887,0.0009028299,0.0007291162,0.0009259488,0.0009756488],"category_scores_gemma":[0.008189199,0.0001657272,0.0007939488,0.001566014,0.0001979544,0.0008333161,0.0004153461,0.0005228746,0.000597701],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005447341,"about_ca_system_score_gemma":0.000543435,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003130263,"about_ca_topic_score_gemma":0.002018705,"domain_scores_codex":[0.9974032,0.0006980788,0.0003361123,0.0003195631,0.00105183,0.0001911548],"domain_scores_gemma":[0.9942861,0.003670969,0.0003221302,0.0002219289,0.001392451,0.0001063362],"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":[0.002657515,0.0005905807,0.03901338,0.001088003,0.0005922776,0.0001734706,0.0002008096,0.1152347,0.01156169,0.0009789279,0.004882434,0.8230262],"study_design_scores_gemma":[0.00007708592,0.001709882,0.02980337,0.0001433866,0.0002528297,0.000495019,0.0003154088,0.935531,0.0255284,0.0009464245,0.00511849,0.00007866007],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8052505,0.02538477,0.1496731,0.0009878972,0.0008531553,0.0002385275,0.001499075,0.003942588,0.01217034],"genre_scores_gemma":[0.9109998,0.003231099,0.08153093,0.000098689,0.0001094965,0.000102581,0.001506311,0.00008845513,0.002332761],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003268089,"threshold_uncertainty_score":0.01728356,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02367686063882455,"score_gpt":0.2826173394445058,"score_spread":0.2589404788056812,"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."}}