The Relationship Between Anthropometric Indices And The Presence Of Hypertension In An Iranian Population Sample Using Data Mining Algorithms

This article discusses the use of machine learning algorithms to identify important anthropometric factors that may predict hypertension (HTN) in adults aged 35-65 years. The study was conducted in northeastern Iran and included 9704 participants. Blood pressure was measured using a sphygmomanometer and HTN was determined as systolic blood pressure (SBP) ≥ 140 mmHg and/or diastolic blood pressure (DBP) ≥ 90. Logistic regression (LR), decision trees (DT), and bootstrap forest (BF) were applied to find the most important anthropometric factors that may predict HTN.