{"id":"W2949767632","doi":"10.1109/access.2019.2923707","title":"Effective Heart Disease Prediction Using Hybrid Machine Learning Techniques","year":2019,"lang":"en","type":"article","venue":"IEEE Access","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":1871,"is_retracted":false,"has_abstract":true,"ca_institutions":"Brandon University","funders":"China Medical University","keywords":"Computer science; Machine learning; Random forest; Heart disease; Artificial intelligence; Predictive modelling; Disease; Internet of Things; Support vector machine; The Internet; Data mining; 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":[0.001012505,0.0005996342,0.0007660741,0.001231066,0.0002303971,0.000521186,0.0006273617,0.0007467149,0.0009159697],"category_scores_gemma":[0.001540778,0.0001965481,0.0007700297,0.000732546,0.0001626564,0.0008267476,0.0003893213,0.0005070618,0.0005229087],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000247675,"about_ca_system_score_gemma":0.0003285329,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002888723,"about_ca_topic_score_gemma":0.003246181,"domain_scores_codex":[0.999559,0.0001429769,0.00002461279,0.00008977401,0.0001340101,0.00004954767],"domain_scores_gemma":[0.9992029,0.0004811635,0.00007197962,0.00006840796,0.000150831,0.00002479794],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003442061,0.0003302056,0.01204995,0.00009146749,0.0001865724,0.0002125118,0.00005377163,0.4951442,0.0137421,0.001462571,0.002307541,0.4740749],"study_design_scores_gemma":[0.000008174045,0.00006711386,0.001205597,0.000005703378,0.00001857324,0.00005747778,0.00000640357,0.9961541,0.001384096,0.0008580176,0.0002258014,0.000008887483],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1789822,0.002013764,0.8128726,0.0005896054,0.0001263351,0.00005632552,0.0003633049,0.002909375,0.002086483],"genre_scores_gemma":[0.8838042,0.0003673173,0.1140098,0.0001421018,0.00009462881,0.00004620028,0.000361278,0.00003564671,0.001138923],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002888723,"threshold_uncertainty_score":0.005743861,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1150902783212159,"score_gpt":0.4976392046669986,"score_spread":0.3825489263457827,"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."}}