{"id":"W7128742363","doi":"10.1109/gitcon65266.2025.11377145","title":"Optimized Feature Selection And Machine Learning Techniques for Early Detection of Chronic Kidney Disease","year":2025,"lang":"","type":"article","venue":"","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Feature selection; Random forest; Categorical variable; Support vector machine; Feature (linguistics); Reliability (semiconductor); Construct (python library); Selection (genetic algorithm); Kidney disease","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":[0.003183719,0.0008168684,0.001185551,0.002274906,0.000317264,0.0007246859,0.0005737287,0.0004849655,0.0005244186],"category_scores_gemma":[0.005734413,0.0002934056,0.001102688,0.001817443,0.0002671635,0.0006907785,0.0005738118,0.00068986,0.0002520641],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000418759,"about_ca_system_score_gemma":0.0007614441,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002187149,"about_ca_topic_score_gemma":0.002058874,"domain_scores_codex":[0.998428,0.0007186894,0.0001283384,0.0002498848,0.0003577059,0.0001175288],"domain_scores_gemma":[0.9982041,0.001109186,0.0001848748,0.0001544389,0.0003143093,0.000033034],"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":[0.0004171433,0.0002825905,0.01926634,0.0001893613,0.0004074438,0.0002439826,0.00009518361,0.1932309,0.01195132,0.00405277,0.003427244,0.7664356],"study_design_scores_gemma":[0.00002831546,0.0001931042,0.009106468,0.00002965339,0.00007639731,0.000161948,0.0000318332,0.97846,0.004416251,0.005950933,0.00151671,0.00002842629],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05276324,0.001496248,0.9439549,0.0002736361,0.00005884009,0.00006815163,0.000223019,0.0006942754,0.0004676202],"genre_scores_gemma":[0.6187779,0.0006970245,0.3784243,0.0001381811,0.0001280621,0.0001510885,0.0008539936,0.00007470679,0.0007546687],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003183719,"threshold_uncertainty_score":0.01683736,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05471903014373603,"score_gpt":0.4169879235585047,"score_spread":0.3622688934147687,"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."}}