@article{998ad168fb4147bab1967d1413fd9b5d,
title = "Artificial Intelligence (AI) approach to identifying factors that determine systolic blood pressure in type 2 diabetes (study from the LOOK AHEAD cohort)",
abstract = "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.",
keywords = "Artificial intelligence, Diabetes mellitus, Predictors, Systolic blood pressure",
author = "Rodhan Khthir and Prasanna Santhanam",
note = "Funding Information: The author wishes to thank the staff and participants of the LOOK AHEAD Study for their valuable contributions.LOOK AHEAD was conducted by the LOOK AHEAD Research Group and supported by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK); the National Heart, Lung, and Blood Institute (NHLBI); the National Institute of Nursing Research (NINR); the National Institute of Minority Health and Health Disparities (NIMHD); the Office of Research on Women's Health (ORWH); and the Centers for Disease Control and Prevention (CDC). The data from LOOK AHEAD were supplied by the NIDDK Central Repositories. This manuscript was not prepared under the auspices of the LOOK AHEAD and did not represent analyses or conclusions of the Look AHEAD Research Group, the NIDDK Central Repository, or the NIH.The data was provided to us in accordance with the NIDDK-NIH researcher data sharing agreement. Publisher Copyright: {\textcopyright} 2021 Diabetes India",
year = "2021",
month = nov,
day = "1",
doi = "10.1016/j.dsx.2021.102278",
language = "English (US)",
volume = "15",
journal = "Diabetes and Metabolic Syndrome: Clinical Research and Reviews",
issn = "1871-4021",
publisher = "Elsevier Ltd",
number = "6",
}