首页|University of Gothenburg Reports Findings in Type 2 Diabetes (Identifying top ten predictors of type 2 diabetes through machine learning analysis of UK Biobank data)
University of Gothenburg Reports Findings in Type 2 Diabetes (Identifying top ten predictors of type 2 diabetes through machine learning analysis of UK Biobank data)
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2024 FEB 02 (NewsRx) – By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Nutritional and Metabolic Diseases and Conditions - Type 2 Diabetes is the subject of a report. According to news reporting originating in Gothenburg, Sweden, by NewsRx journalists, research stated, “The study aimed to identify the most predictive factors for the development of type 2 diabetes. Using an XGboost classification model, we projected type 2 diabetes incidence over a 10-year horizon.” The news reporters obtained a quote from the research from the University of Gothenburg, “We delib- erately minimized the selection of baseline factors to fully exploit the rich dataset from the UK Biobank. The predictive value of features was assessed using shap values, with model performance evaluated via Receiver Operating Characteristic Area Under the Curve, sensitivity, and specificity. Data from the UK Biobank, encompassing a vast population with comprehensive demographic and health data, was employed. The study enrolled 450,000 participants aged 40-69, excluding those with pre-existing diabetes. Among 448,277 participants, 12,148 developed type 2 diabetes within a decade. HbA1c emerged as the foremost predictor, followed by BMI, waist circumference, blood glucose, family history of diabetes, gamma-glutamyl transferase, waist-hip ratio, HDL cholesterol, age, and urate. Our XGboost model achieved a Receiver Op- erating Characteristic Area Under the Curve of 0.9 for 10-year type 2 diabetes prediction, with a reduced 10-feature model achieving 0.88. Easily measurable biological factors surpassed traditional risk factors like diet, physical activity, and socioeconomic status in predicting type 2 diabetes. Furthermore, high predic- tion accuracy could be maintained using just the top 10 biological factors, with additional ones offering marginal improvements.”
GothenburgSwedenEuropeBiological FactorsCyborgsEmerging TechnologiesHealth and MedicineMachine LearningNon-Insulin Dependent Diabetes MellitusNutritional and Metabolic Diseases and ConditionsRisk and PreventionType 2 Diabetes
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