{"id":"W4389454899","doi":"10.1016/j.heliyon.2023.e23219","title":"Performance and explainability of feature selection-boosted tree-based classifiers for COVID-19 detection","year":2023,"lang":"en","type":"article","venue":"Heliyon","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Agencia Estatal de Investigación; European Commission; European Regional Development Fund; Ministerio de Ciencia, Innovación y Universidades; Comunidad de Madrid","keywords":"Random forest; Feature selection; Artificial intelligence; Gradient boosting; Receiver operating characteristic; Machine learning; Computer science; Boosting (machine learning); Decision tree; Coronavirus disease 2019 (COVID-19); Feature (linguistics); Pattern recognition (psychology); 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":[0.01868263,0.002052545,0.00210332,0.003423423,0.0006707189,0.001430355,0.001294708,0.001984656,0.0008142561],"category_scores_gemma":[0.02276796,0.0003152911,0.00177562,0.001385566,0.0005093689,0.00155582,0.001074614,0.001727222,0.0007414769],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009082785,"about_ca_system_score_gemma":0.0013919,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004336475,"about_ca_topic_score_gemma":0.002525016,"domain_scores_codex":[0.9954372,0.002075262,0.0004100083,0.0007852248,0.0009160392,0.0003762548],"domain_scores_gemma":[0.978282,0.01662712,0.00119289,0.001211494,0.002324417,0.0003620558],"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":[0.002528674,0.0009986797,0.1446689,0.000427041,0.001303987,0.0003805832,0.0002390024,0.3852934,0.005377673,0.001308879,0.006005997,0.4514672],"study_design_scores_gemma":[0.00004374732,0.0004194996,0.01010006,0.00003649277,0.0001410182,0.0001445499,0.00005045852,0.9849573,0.002585688,0.001009074,0.0004857079,0.00002628681],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7010564,0.006818359,0.2818049,0.001198671,0.0004365269,0.0003853244,0.001740595,0.003731803,0.002827456],"genre_scores_gemma":[0.9394991,0.0005627293,0.05666663,0.0001804002,0.0001241238,0.0001222065,0.002044128,0.000063839,0.0007369659],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01868263,"threshold_uncertainty_score":0.09880435,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03859797154997367,"score_gpt":0.326679657654826,"score_spread":0.2880816861048523,"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."}}