{"id":"W4313245257","doi":"10.3390/bioengineering10010025","title":"An Efficient Approach to Predict Eye Diseases from Symptoms Using Machine Learning and Ranker-Based Feature Selection Methods","year":2022,"lang":"en","type":"article","venue":"Bioengineering","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Machine learning; Feature selection; Artificial intelligence; Computer science; Random forest; Naive Bayes classifier; Logistic regression; Glaucoma; Decision tree; AdaBoost; Cataracts; Boosting (machine learning); Cross-validation; Medicine; Ophthalmology; Support vector machine","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":[],"consensus_categories":[],"category_scores_codex":[0.0002314462,0.0001549752,0.0002494958,0.0002476385,0.0002151641,0.00004244844,0.00005517269,0.00002853676,0.00002043812],"category_scores_gemma":[0.0000619992,0.0001455375,0.00007595256,0.0005037407,0.00001403108,0.00002831026,0.00004267435,0.0002948924,3.363321e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001101467,"about_ca_system_score_gemma":0.00002527835,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001458466,"about_ca_topic_score_gemma":2.371144e-7,"domain_scores_codex":[0.9989942,0.0001214836,0.000122854,0.000333562,0.0002285832,0.0001992814],"domain_scores_gemma":[0.9995666,0.0000399015,0.00003597565,0.0001278311,0.0000276573,0.0002020149],"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":[0.00007069641,0.0001208144,0.07613051,0.00005268914,0.00008199905,0.000005977957,0.0001208133,0.77051,0.1504634,0.000002403689,0.000008011129,0.002432719],"study_design_scores_gemma":[0.0004791103,0.0001555822,0.01189353,0.00003769958,0.0003398178,0.00001489336,0.0001178781,0.9832677,0.002423605,4.579717e-7,0.001121893,0.0001478629],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6770004,0.001167007,0.3213125,0.00009896082,0.00006037729,0.0001300818,0.00002210523,0.0001932129,0.00001529606],"genre_scores_gemma":[0.8832185,0.000002841429,0.1163435,0.00005548492,0.0001242461,0.00002145446,0.0001407416,0.00003305981,0.00006009861],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2127577,"threshold_uncertainty_score":0.5934848,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01055897565576985,"score_gpt":0.2991507603349504,"score_spread":0.2885917846791805,"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."}}