{"id":"W4200042763","doi":"10.1109/ictc52510.2021.9620780","title":"An Evaluation of Machine Learning Classifiers for Prediction of Alzheimer's Disease, Mild Cognitive Impairment and Normal Cognition","year":2021,"lang":"en","type":"article","venue":"2021 International Conference on Information and Communication Technology Convergence (ICTC)","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Ministry of Science and ICT, South Korea; National Research Foundation","keywords":"Dementia; Machine learning; Naive Bayes classifier; Artificial intelligence; Support vector machine; Random forest; Decision tree; Computer science; Disease; Cognition; Alzheimer's disease; Statistical classification; Logistic regression; Supervised learning; Medicine; Artificial neural network; Psychiatry; Pathology","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":[0.008179414,0.001312028,0.001169563,0.002419365,0.0005971133,0.001152647,0.0009567632,0.001393633,0.0008781056],"category_scores_gemma":[0.01281582,0.0002200826,0.0008994224,0.00113608,0.0002973464,0.001148277,0.0005882342,0.0008741624,0.0004162613],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001076303,"about_ca_system_score_gemma":0.001466727,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01174679,"about_ca_topic_score_gemma":0.006529023,"domain_scores_codex":[0.9974402,0.0009492598,0.0003097193,0.0004316556,0.000667577,0.0002015486],"domain_scores_gemma":[0.9915828,0.005605681,0.0003509054,0.0003732198,0.001791596,0.000295739],"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":[0.008165525,0.004844856,0.2558253,0.0007972086,0.001539742,0.0002815465,0.0002058138,0.1965303,0.003620004,0.00123229,0.0157525,0.511205],"study_design_scores_gemma":[0.0001707957,0.002264668,0.04015711,0.0001055149,0.0002510943,0.0001137334,0.0002093614,0.952714,0.002222566,0.0005025194,0.001258212,0.00003041295],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9655667,0.006476495,0.01822756,0.0007704241,0.0006663647,0.0004559911,0.002678984,0.0006929533,0.004464556],"genre_scores_gemma":[0.9766591,0.000904809,0.01684441,0.0001347702,0.0001135155,0.0001675891,0.004138385,0.00001880118,0.001018579],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01174679,"threshold_uncertainty_score":0.04325736,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1649139899741452,"score_gpt":0.4376043587043143,"score_spread":0.2726903687301691,"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."}}