{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005670496,0.00007102096,0.0001469716,0.0001728508,0.0001414981,0.00004366966,0.0002545607,0.0000306032,0.000002277153],"category_scores_gemma":[0.00001314509,0.00005726013,0.00002440039,0.0003619171,0.00009666172,0.000255524,0.00003438184,0.0001250926,1.533079e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002817644,"about_ca_system_score_gemma":0.00003295891,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007682677,"about_ca_topic_score_gemma":0.000004752625,"domain_scores_codex":[0.9993331,0.000004772776,0.0002312838,0.0001165637,0.0001904443,0.0001238454],"domain_scores_gemma":[0.9994959,0.00004965254,0.000231491,0.00005450248,0.0001347223,0.00003374451],"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":[0.00003674587,0.00001578038,0.003243766,0.00006481144,0.00001407528,7.953509e-8,0.000412986,0.01782331,0.2967083,0.000509512,0.000009260461,0.6811614],"study_design_scores_gemma":[0.00011729,0.0004632215,0.002937467,0.00006454751,0.000005692262,0.00004138137,0.00002436217,0.5029489,0.4928541,0.00002971421,0.0004405919,0.00007270224],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.458691,0.0001530051,0.5405926,0.00006325221,0.0003330561,0.00005031048,8.159426e-7,0.00003064375,0.00008534155],"genre_scores_gemma":[0.9523074,0.00003547324,0.04744152,0.000006039704,0.0001991705,0.000003621115,2.161475e-8,0.000003655946,0.000003087478],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6810887,"threshold_uncertainty_score":0.2335,"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."}}