{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006734252,0.000150275,0.0003198458,0.00008878964,0.001234874,0.00003295669,0.000045559,0.0001326761,0.00003716871],"category_scores_gemma":[0.0006103783,0.0001352208,0.00002290211,0.00007783996,0.00006367837,0.0001995977,0.0002370596,0.0004684476,0.000002869499],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006295475,"about_ca_system_score_gemma":0.00007104456,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003273693,"about_ca_topic_score_gemma":0.001116871,"domain_scores_codex":[0.9984767,0.0001982904,0.0004328012,0.0003536054,0.0001082516,0.000430289],"domain_scores_gemma":[0.9984148,0.001028648,0.0001448861,0.0001192626,0.0001189595,0.0001735185],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003588128,0.000004594458,0.9879822,0.0007757529,0.000006363582,3.040029e-7,0.00138104,0.00001394822,0.001650601,0.0001166656,0.000007326453,0.008025286],"study_design_scores_gemma":[0.0003835304,0.000360533,0.09251094,0.00162421,0.000060293,0.00002031468,0.01869844,0.8775419,0.004696473,0.0008289531,0.002823615,0.0004508265],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9946238,0.0009584881,0.001945191,0.0003520967,0.0001480607,0.001378296,0.000009101434,0.0002686077,0.0003163231],"genre_scores_gemma":[0.9876426,0.00008384298,0.0116211,0.0002542821,0.0001711594,0.00005171911,0.000003879728,0.00003429572,0.0001370948],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8954713,"threshold_uncertainty_score":0.9497777,"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."}}