{"id":"W2552509183","doi":"10.1109/fuzz-ieee.2016.7737850","title":"Building a Cardiovascular Disease predictive model using Structural Equation Model & Fuzzy Cognitive Map","year":2016,"lang":"en","type":"article","venue":"","topic":"Cognitive Science and Mapping","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"Health Canada; Lakehead University; Public Health Agency of Canada","keywords":"Fuzzy cognitive map; Structural equation modeling; Computer science; Fuzzy logic; Machine learning; Agency (philosophy); Artificial intelligence; Transparency (behavior); Data modeling; Data mining; Fuzzy set; Membership function","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.001714226,0.0006780283,0.0005242647,0.002565943,0.000741765,0.001455811,0.001075659,0.0008323142,0.002459009],"category_scores_gemma":[0.004127452,0.0003638139,0.001189086,0.001569838,0.000313185,0.0009631549,0.000827747,0.001037373,0.0004133415],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001428929,"about_ca_system_score_gemma":0.002109187,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05143655,"about_ca_topic_score_gemma":0.0333748,"domain_scores_codex":[0.9993958,0.0002230633,0.00003036808,0.0001528044,0.0001296337,0.00006827919],"domain_scores_gemma":[0.998949,0.0006724298,0.00008269036,0.00003851511,0.0002209288,0.00003654273],"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":[0.0002631454,0.0006512259,0.110542,0.0003117509,0.000700081,0.0006395836,0.001381878,0.6732094,0.001088235,0.03008979,0.00794952,0.1731733],"study_design_scores_gemma":[0.00001671233,0.00005120636,0.004352669,0.00002652402,0.00008006047,0.00004936467,0.0001633426,0.9848547,0.0001793486,0.008998257,0.001206025,0.00002185211],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3961271,0.0005332615,0.5871916,0.003264491,0.0001704443,0.0004550866,0.002675709,0.001085848,0.008496379],"genre_scores_gemma":[0.8699955,0.0003177146,0.1257505,0.0001378162,0.00005165047,0.0003688867,0.001262818,0.00002310258,0.002092011],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05143655,"threshold_uncertainty_score":0.1022743,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0699173623660079,"score_gpt":0.2886192257197032,"score_spread":0.2187018633536953,"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."}}