Machine learning approach to predicting albuminuria in persons with type 2 diabetes: An analysis of the LOOK AHEAD Cohort
| Autor: | Tanmay Nath, Zeid J. Khitan, Prasanna Santhanam |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Endocrinology
Diabetes and Metabolism Renal function Type 2 diabetes Machine learning computer.software_genre albuminuria metabolic syndrome Machine Learning chemistry.chemical_compound Risk Factors Diabetes mellitus Internal Medicine Short Research Article Humans Medicine Creatinine diabetes business.industry medicine.disease Short Research Articles Blood pressure Diabetes Mellitus Type 2 chemistry Hypertension Albuminuria Lean body mass Artificial intelligence proteinuria medicine.symptom Metabolic syndrome Cardiology and Cardiovascular Medicine business computer Glomerular Filtration Rate |
| Zdroj: | The Journal of Clinical Hypertension |
| ISSN: | 1751-7176 1524-6175 |
| Popis: | Albuminuria and estimated glomerular filtration rate (e‐GFR) are early markers of renal disease and cardiovascular outcomes in persons with diabetes. Although body composition has been shown to predict systolic blood pressure, its application in predicting albuminuria is unknown. In this study, we have used machine learning methods to assess the risk of albuminuria in persons with diabetes using body composition and other determinants of metabolic health. This study is a comparative analysis of the different methods to predict albuminuria in persons with diabetes mellitus who are older than 40 years of age, using the LOOK AHEAD study cohort‐baseline characteristics. Age, different metrics of body composition, duration of diabetes, hemoglobin A1c, serum creatinine, serum triglycerides, serum cholesterol, serum HDL, serum LDL, maximum exercise capacity, systolic blood pressure, diastolic blood pressure, and the ankle‐brachial index are used as predictors of albuminuria. We used Area under the curve (AUC) as a metric to compare the classification results of different algorithms, and we show that AUC for the different models are as follows: Random forest classifier‐0.65, gradient boost classifier‐0.61, logistic regression‐0.66, support vector classifier ‐0.61, multilayer perceptron ‐0.67, and stacking classifier‐0.62. We used the Random forest model to show that the duration of diabetes, A1C, serum triglycerides, SBP, Maximum exercise Capacity, serum creatinine, subtotal lean mass, DBP, and subtotal fat mass are important features for the classification of albuminuria. In summary, when applied to metabolic imaging (using DXA), machine learning techniques offer unique insights into the risk factors that determine the development of albuminuria in diabetes. |
| Databáze: | OpenAIRE |
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