Heart Rate Variability measured during rest and after orthostatic challenge to detect autonomic dysfunction in Type 2 Diabetes Mellitus using the Classification and Regression Tree model

• Published in: Technology and health care Vol. 30; no. 2; pp. 361 - 378
• Main Authors: Rathod, Shashikant, Phadke, Leena, Chaskar, Uttam, Patil, Chetankumar
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.01.2022; Sage Publications Ltd
• Subjects: Adult; Algorithms; Autonomic nervous system; Autonomic Nervous System Diseases; Classification; Complications; Diabetes; Diabetes mellitus; Diabetes mellitus (non-insulin dependent); Diabetes Mellitus, Type 2 - complications; Diabetic Neuropathies - diagnosis; Dysautonomia; EKG; Female; Heart rate; Heart Rate - physiology; Humans; India; Learning algorithms; Machine learning; Male; Regression analysis; Regression models; Sensitivity; Variability; India; Heart Rate Variability (HRV); orthostatic challenge; machine learning; Type 2 diabetes mellitus (T2DM); classification and regression tree (CART); autonomic dysfunction

BACKGROUND: According to the World Health Organization, one in ten adults will have Type 2 Diabetes Mellitus (T2DM) in the next few years. Autonomic dysfunction is one of the significant complications of T2DM. Autonomic dysfunction is usually assessed by standard Ewing’s test and resting Heart Rate...