{"id":"W3008157143","doi":"10.1109/bigdata47090.2019.9005488","title":"Towards comparing and using Machine Learning techniques for detecting and predicting Heart Attack and Diseases","year":2019,"lang":"en","type":"article","venue":"","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":68,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Random forest; Machine learning; Artificial intelligence; Logistic regression; Computer science; Heart disease; Naive Bayes classifier; Preprocessor; Bayesian network; Decision tree; Medical record; Disease; Data pre-processing; Data mining; Medical emergency; Medicine; Support vector machine; Internal medicine","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":[0.01201766,0.001408036,0.001256145,0.004735427,0.0005830564,0.003446469,0.001449907,0.002172442,0.0007126307],"category_scores_gemma":[0.01902955,0.0003155723,0.00130579,0.002286827,0.0004611805,0.003482881,0.0009635873,0.001398824,0.0007233846],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006805566,"about_ca_system_score_gemma":0.0009611575,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004309725,"about_ca_topic_score_gemma":0.003173738,"domain_scores_codex":[0.9898961,0.004520652,0.001020069,0.001644395,0.002558354,0.0003605338],"domain_scores_gemma":[0.986678,0.00800051,0.0008265466,0.000962267,0.003298417,0.0002342],"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":[0.000973812,0.001942835,0.1384916,0.001078119,0.001093474,0.0002706622,0.0005545053,0.0580333,0.01689488,0.003282523,0.004450681,0.7729337],"study_design_scores_gemma":[0.0001343666,0.002982929,0.0761935,0.0006863093,0.001074795,0.0006323048,0.00179873,0.8667243,0.02871785,0.007298399,0.01353116,0.0002253802],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5473722,0.02193139,0.4061604,0.002895703,0.0009751534,0.001004884,0.001304681,0.003559974,0.01479566],"genre_scores_gemma":[0.7169492,0.004290148,0.2741522,0.0005097284,0.0002501679,0.0002331806,0.001571639,0.00006813868,0.001975525],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.01201766,"threshold_uncertainty_score":0.06355625,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2260282901277841,"score_gpt":0.5031597853022041,"score_spread":0.27713149517442,"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."}}