{"id":"W4407164896","doi":"10.1016/j.mex.2025.103210","title":"MapReduce based big data framework using associative Kruskal poly Kernel classifier for diabetic disease prediction","year":2025,"lang":"en","type":"article","venue":"MethodsX","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Artificial intelligence; Associative property; Kernel (algebra); Big data; Classifier (UML); Pattern recognition (psychology); Machine learning; Data mining; Mathematics","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":["metaresearch","metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.003620445,0.0002555967,0.0004474234,0.0002205012,0.001483315,0.00002841603,0.0006254165,0.00053468,0.0001208983],"category_scores_gemma":[0.0172229,0.0002513647,0.0001187587,0.000652832,0.0001564569,0.0001872727,0.000350469,0.001082205,0.00004160674],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007507487,"about_ca_system_score_gemma":0.00223217,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008602802,"about_ca_topic_score_gemma":0.0001513421,"domain_scores_codex":[0.9940038,0.003084384,0.0009265058,0.0008045002,0.0003302234,0.0008505834],"domain_scores_gemma":[0.9874023,0.01008816,0.000394839,0.001336456,0.0004695912,0.000308653],"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":[0.001106568,0.0003867004,0.690892,0.002327016,0.0002396409,0.000007715187,0.0052183,0.000276159,0.001215353,0.00943123,0.01179825,0.277101],"study_design_scores_gemma":[0.000612924,0.000107365,0.1108487,0.002926185,0.0005783726,1.108946e-7,0.006254273,0.7035034,0.001569768,0.1422322,0.03079236,0.0005742917],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03880211,0.000767532,0.9395132,0.006666156,0.00878825,0.002445789,0.002362473,0.000221236,0.0004332592],"genre_scores_gemma":[0.4371541,0.00003557037,0.5469039,0.007968164,0.003434605,0.001040861,0.0005226876,0.0001197405,0.002820401],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7032273,"threshold_uncertainty_score":0.9999939,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.553412797937658,"score_gpt":0.5924231524663424,"score_spread":0.0390103545286844,"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."}}