Performance evaluation of classification algorithms by excluding the most relevant attributes for dipperon-dipper pattern estimation in Type-2 DM patients

摘要

Diabetes Mellitus (DM) is a high prevalence disease that causes cardiovascular morbidity and mortality. On the other hand, the absence of physiologic night-time blood pressure decrease can further lead to morbidity problems such as target organ damage both in diabetics and non-diabetics patients. However, the Non-dipping pattern can only be measured by the 24-hour ambulatory blood pressure monitoring (ABPM) device. ABPM has certain challenges such as insufficient devices to distribute to patients, lack of trained staff or high costs. Therefore, in this study, it is aimed to develop a classifier model that can achieve a sufficiently high accuracy percentage for Dipperon-Dipper blood pressure pattern in patients by excluding ABPM data.