{"id":"W3123604007","doi":"10.4018/ijeach.2021010101","title":"Prediction of Heart Cancer Data Using Hybrid Optimization and Machine Learning Techniques","year":2021,"lang":"en","type":"article","venue":"International Journal of Extreme Automation and Connectivity in Healthcare","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Laurentian University","funders":"","keywords":"Overfitting; Particle swarm optimization; Computer science; Identification (biology); Artificial intelligence; Data set; Machine learning; Cancer; Set (abstract data type); Hybrid algorithm (constraint satisfaction); Data mining; Artificial neural network; 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":[0.001284649,0.0008005099,0.001030354,0.001292876,0.0002094753,0.0007500598,0.0004875322,0.0008168879,0.0006340472],"category_scores_gemma":[0.002208111,0.0002844651,0.0009572606,0.0008874468,0.0002447218,0.0004247779,0.0003717427,0.0005681628,0.0001636888],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004856993,"about_ca_system_score_gemma":0.000586279,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007587621,"about_ca_topic_score_gemma":0.003400734,"domain_scores_codex":[0.9996455,0.0001226577,0.00003379627,0.00009004511,0.00007083059,0.00003713664],"domain_scores_gemma":[0.9988568,0.0007723403,0.0001075286,0.0000553155,0.0001773376,0.00003071688],"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":[0.0001987698,0.0001171951,0.006635164,0.00006244247,0.00007602688,0.00008436823,0.00002774469,0.953459,0.001969607,0.0003020017,0.0005612511,0.03650649],"study_design_scores_gemma":[0.000002818274,0.00001515966,0.0007193221,0.000001636603,0.000003005968,0.00000605708,0.000003951417,0.998791,0.0003174877,0.000102148,0.00003510881,0.000002321463],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5559998,0.001382409,0.4378113,0.0005182121,0.00008318834,0.0001197403,0.0007016356,0.001700093,0.001683605],"genre_scores_gemma":[0.9271845,0.000218222,0.07068421,0.0000640988,0.00002182,0.0001026333,0.0007682802,0.00004478122,0.0009115264],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007587621,"threshold_uncertainty_score":0.01508689,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3281206292536224,"score_gpt":0.4758600065653155,"score_spread":0.1477393773116931,"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."}}