{"id":"W4366089059","doi":"10.3390/app13085012","title":"Comparative Analysis of Supervised Machine and Deep Learning Algorithms for Kyphosis Disease Detection","year":2023,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Princess Nourah Bint Abdulrahman University","keywords":"Hyperparameter; Kyphosis; Support vector machine; Artificial intelligence; Naive Bayes classifier; Machine learning; Computer science; Random forest; Cross-validation; Logistic regression; Artificial neural network; Statistics; Medicine; Mathematics; Surgery","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":[0.004804141,0.00129356,0.001251791,0.00237536,0.0004981454,0.001161268,0.0009819968,0.001225804,0.001365034],"category_scores_gemma":[0.01056551,0.0002907224,0.00112668,0.001153734,0.0003447483,0.001333822,0.0006295189,0.000923734,0.0004151094],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001364005,"about_ca_system_score_gemma":0.001618221,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01072916,"about_ca_topic_score_gemma":0.007381325,"domain_scores_codex":[0.9972693,0.0009048604,0.0003207714,0.0005052026,0.0007469596,0.0002529093],"domain_scores_gemma":[0.9922445,0.004718265,0.0003685229,0.0004250508,0.002067288,0.0001763174],"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":[0.001283442,0.0005813788,0.02278098,0.0005460928,0.0006685432,0.0001723591,0.0001260046,0.4482279,0.002234807,0.003036047,0.008464288,0.5118781],"study_design_scores_gemma":[0.00002502586,0.0001621241,0.003108782,0.00003225238,0.00005063204,0.00004216352,0.00004007097,0.9932863,0.001267202,0.001244119,0.0007262987,0.00001503739],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.6490921,0.02248643,0.3068391,0.001720322,0.0009502763,0.0003775805,0.001890001,0.00542151,0.01122271],"genre_scores_gemma":[0.906334,0.002197905,0.08451267,0.0002774881,0.0001758417,0.0001713678,0.003380374,0.0001590308,0.00279135],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.01072916,"threshold_uncertainty_score":0.02540702,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02677913621300018,"score_gpt":0.2826946303811358,"score_spread":0.2559154941681356,"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."}}