Advanced method used for hypertension’s risk factors stratification: support ‎vector machines and gravitational search algorithm

Publish Year: 1394
نوع سند: مقاله ژورنالی
زبان: English
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شناسه ملی سند علمی:

JR_RYA-11-6_006

تاریخ نمایه سازی: 2 شهریور 1401

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 ۱۹ years or over from ۲۰۰۱ to ۲۰۰۷. 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 ۲۴-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.

Authors

Alireza Khosravi

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

Amin Gharipour

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

Mojgan Gharipour

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

Mohammadreza Khosravi

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

Elham Andalib

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

Shahin Shirani

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

Mohsen Mirmohammadsedeghi

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