{"id":"W4312710248","doi":"10.1007/978-3-031-21385-4_19","title":"ML_SPS: Stroke Prediction System Employing Machine Learning Approach","year":2022,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Random forest; Gradient boosting; Decision tree; Stroke (engine); Machine learning; Computer science; Artificial intelligence; Boosting (machine learning); Ensemble learning; Engineering","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":[0.0001739674,0.0006035857,0.00063527,0.0008976801,0.0002394813,0.0004949537,0.0007975598,0.0004608273,0.009071405],"category_scores_gemma":[0.0003201023,0.0001941037,0.0003675137,0.0005131104,0.00007605713,0.0005092572,0.0003853326,0.0003836301,0.007019798],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002615489,"about_ca_system_score_gemma":0.0004817429,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0046935,"about_ca_topic_score_gemma":0.004761954,"domain_scores_codex":[0.9999249,0.000005684794,0.000007375728,0.00002318676,0.00002992685,0.000008966913],"domain_scores_gemma":[0.9998989,0.0000197668,0.000007705274,0.00001149752,0.00004807955,0.00001405134],"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":[0.001078647,0.0003345736,0.01591679,0.0003091288,0.0001092921,0.0004882002,0.00004813958,0.01042356,0.01180828,0.0007438308,0.2377111,0.7210286],"study_design_scores_gemma":[0.0004846896,0.001075507,0.05805289,0.0001807757,0.0004882789,0.003046669,0.0001458903,0.6855732,0.0977642,0.005765888,0.1471657,0.0002562646],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1653414,0.004455521,0.3984115,0.001970772,0.001483408,0.00113287,0.08688508,0.2928241,0.04749531],"genre_scores_gemma":[0.5385852,0.003580986,0.2340163,0.001693154,0.0009635946,0.001094203,0.1237602,0.00156358,0.09474275],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009071405,"threshold_uncertainty_score":0.03034687,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1915647849268315,"score_gpt":0.4246137521139707,"score_spread":0.2330489671871391,"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."}}