Diabetes & metabolic syndrome · 2021
Artificial Intelligence (AI) approach to identifying factors that determine systolic blood pressure in type 2 diabetes (study from the LOOK AHEAD cohort).
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Khthir Rodhan, Santhanam Prasanna
Аннотация
BACKGROUND AND AIMS: Artificial Intelligence (AI) methods have recently become critical for research in diabetes in the era of big-data science.
METHODS: In this study, we used the data from the LOOK AHEAD and applied Random Forest to examine the factors determining SBP in persons with diabetes using the software R (version 4.0.3).
RESULTS: Our analysis (that included 4723 participants) showed that maximal exercise capacity, age, albumin to creatinine ratio, and serum creatinine were the key variables that determined systolic blood pressure.
CONCLUSIONS: Maximum exercise capacity is an important predictor of systolic blood pressure in patients with type 2 diabetes.
METHODS: In this study, we used the data from the LOOK AHEAD and applied Random Forest to examine the factors determining SBP in persons with diabetes using the software R (version 4.0.3).
RESULTS: Our analysis (that included 4723 participants) showed that maximal exercise capacity, age, albumin to creatinine ratio, and serum creatinine were the key variables that determined systolic blood pressure.
CONCLUSIONS: Maximum exercise capacity is an important predictor of systolic blood pressure in patients with type 2 diabetes.