Document Type : Original Article(s)

Authors

• Alireza Khosravi ¹
• Amin Gharipour ²
• Mojgan Gharipour ³
• Mohammadreza Khosravi ⁴
• Elham Andalib ⁵
• Shahin Shirani ⁶
• Mohsen Mirmohammadsedeghi ⁷

¹ Associate Professor, Interventional Cardiology Research Center, Cardiovascular Research Institute, Isfahan University of Medical ‎Sciences, Isfahan, Iran

² Department of Artificial Intelligence, School of Information and Communication Technology, Griffith University, Gold Coast, Australia

³ PhD Candidate, Isfahan Cardiovascular Research Center, Cardiovascular Research Institute, Isfahan University of Medical Sciences, ‎Isfahan, Iran

⁴ Associate Professor, Cardiac Rehabilitation Research Center, Cardiovascular Research Institute, Isfahan University of Medical Sciences, ‎Isfahan, Iran

⁵ General Practitioner, Heart Failure Research Center, Cardiovascular Research Institute, Isfahan University of Medical Sciences, Isfahan, ‎Iran

⁶ Department of Cardiology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran

⁷ Associate Professor, Hypertension Research Center, Cardiovascular Research Institute, Isfahan University of Medical Sciences, Isfahan, Iran

Abstract

BACKGROUND: The aim of this study is to present an objective method based on support vector machines (SVMs) and gravitational search algorithm (GSA) which is initially utilized for recognition the pattern among risk factors and hypertension (HTN) to stratify and analysis HTN’s risk factors in an Iranian urban population. METHODS: This community-based and cross-sectional research has been designed based on the probabilistic sample of residents of Isfahan, Iran, aged 19 years or over from 2001 to 2007. One of the household members was randomly selected from different age groups. Selected individuals were invited to a predefined health center to be educated on how to collect 24-hour urine sample as well as learning about topographic parameters and blood pressure measurement. The data from both the estimated and measured blood pressure [for both systolic blood pressure (SBP) and diastolic blood pressure (DBP)] demonstrated that optimized SVMs have a highest estimation potential. RESULTS: This result was particularly more evident when SVMs performance is evaluated with regression and generalized linear modeling (GLM) as common methods. Blood pressure risk factors impact analysis shows that age has the highest impact level on SBP while it falls second on the impact level ranking on DBP. The results also showed that body mass index (BMI) falls first on the impact level ranking on DBP while have a lower impact on SBP. CONCLUSION: Our analysis suggests that salt intake could efficiently influence both DBP and SBP with greater impact level on SBP. Therefore, controlling salt intake may lead to not only control of HTN but also its prevention.

Keywords

• Support Vector Machines
• Gravitational Search Algorithm
• High Blood Pressure