[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117874-en":3,"doc-seo-117874-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":20,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},117874,549768064778,"Finn","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","AutoPrognosis 2.0 - Diagnostic and Prognostic Modeling Feature Set - Table S1","Table S1 summarizes the most important predictive features ranked by effect size for AutoPrognosis 2.0 in healthcare diagnostics and prognostics. The table lists metabolic measures (HbA1c, glucose), anthropometric indicators (waist/height ratio, waist size, BMI, weight), liver and inflammation markers (alanine transaminase, CRP, gamma-glutamyl transferase), renal-related signals (cystatin-c, chronic kidney disease, urine microalbumin), lipid-related indices (triglycerides, HDL), medication and clinical history features (anti-hypertensives, lipid-lowering drugs), and cardiovascular outcomes (atrial fibrillation, hypertension history).","Table S1: Most important features as measured by effect size.  \n\n| Feature | Effect size |\n| --- | --- |\n| HbA1c | 3.0 |\n| Glucose | 3.0 |\n| Waist/Height Ratio | 1.7 |\n| Waist Size | 1.6 |\n| Body Mass Index | 1.6 |\n| Weight | 1.4 |\n| Hip Size | 1.2 |\n| Waist/Hip Ratio | 1.2 |\n| Alanine Transaminase | 0.95 |\n| Triglycerides | 0.88 |\n| Cystatin-c | 0.86 |\n| CRP | 0.83 |\n| Gamma-Glutamyl Transferase | 0.82 |\n| Anti-Hypertensives | 0.77 |\n| Uric Acid | 0.73 |\n| HDL | 0.73 |\n| Atrial Fibrillation | 0.72 |\n| Sex Hormone-Binding Globulin | 0.72 |\n| History of Hypertension | 0.69 |\n| Chronic Kidney Disease | 0.68 |\n| Urine Microalbumin | 0.65 |\n| Red Blood Cell Count | 0.64 |\n| Aspartate Aminotransferase | 0.63 |\n| Systolic Blood Pressure | 0.63 |\n| Lipid-Lowering Drugs | 0.63 |","cbCaiecGVbqDsfjq","https://ap.wps.com/l/cbCaiecGVbqDsfjq","pdf",74982,1,"English","en",105,"# Table S1: Most important features\n## Ranked feature list by effect size","[{\"question\":\"What does Table S1 report in this document?\",\"answer\":\"Table S1 reports the most important predictive features ranked by effect size for AutoPrognosis 2.0.\"},{\"question\":\"Which categories of features appear among the most important ones?\",\"answer\":\"The table includes glycemic markers (e.g., HbA1c, glucose), body measurements (e.g., waist/height ratio, BMI), biomarkers of inflammation and liver function (e.g., CRP, alanine transaminase), renal-related measures (e.g., cystatin-c, chronic kidney disease), and medication or clinical history features (e.g., anti-hypertensives, lipid-lowering drugs).\"},{\"question\":\"How are features compared in the table?\",\"answer\":\"Features are compared using effect size, with larger values indicating more important features in the ranking.\"}]","AutoPrognosis 2.0 - 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