Medical Journal of the Islamic Republic Of Iran

en A Study on Prevalence and Factors Affecting Hypertension in an Iranian Population: Results from the Fasa Cohort Study Biostatistics and Epidemiology Biostatistics and Epidemiology Original Research Original Research Background: In recent years, hypertension has been one of the most important noncommunicable diseases worldwide. In this context, identifying the predictors of this disease can help health policymakers to reduce its burden. This study aimed to identify some of the most important influential factors of hypertension and present a model to predict this disease in the data from a large sample cohort study.    Methods: The data set included 10,138 people from the baseline phase of the Fasa cohort study during 2014 and 2016. The main outcome under study was having hypertension in the baseline phase of the study according to self-reports or medical examinations. To identify the related factors of hypertension, logistic regression, classification tree, and random forest models were utilized. Statistical analyses were performed in R.    Results: Among the 10,138 people examined, 2819 (27.8%) had hypertension. In the initial screening, 39 variables were regarded as potential indicators of hypertension. After preliminary analysis, 11 variables were recognized as important predictors based on the importance index: history of cardiovascular disease, cardiac disease, waist circumference to height ratio, body mass index, sex, hypertension in a first-degree relative, weight, fatty liver, cardiac disease in a first-degree relative, diabetes in a first-degree relative, and energy intake. The area under the receiving operating characteristic (ROC) curve for predicting hypertension using logistic regression, classification tree, and random forest models was about 72.8%, 73%, and 87.6%, respectively. Also, the accuracy of these models was 65.2%, 67.4% and 77.8%, respectively.    Conclusion: In general, our findings showed that machine learning-based approaches, such as random forest models, outperformed classical methods, such as logistic regression in predicting hypertension. Regarding the rather high prevalence of hypertension in the population under study, there is an urgent need to pay more attention to its indicators for early diagnosis of the patients and reducing the burden of this silent disease in our country.   Hypertension, Related Predictors, Random Forest Model, Fasa Cohort Study 883 891 http://mjiri.iums.ac.ir/browse.php?a_code=A-10-8835-1&slc_lang=en&sid=1 Seyede Melika Taheri ghaleno melitaheri77@gmail.com 200319475328460089647 0009-0004-6951-4306 No Department of Biostatistics, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran Abdollah Safari a.safari@ut.ac.ir 200319475328460089648 200319475328460089648 No School of Mathematics, Statistics, and Computer Science, Faculty of Science, University of Tehran, Iran Reza Homayounfar r_homayounfar@yahoo.com 200319475328460089649 200319475328460089649 No National Nutrition and Food Technology Research Institute, Shahid Beheshti University of Medical Sciences, Tehran, Iran Mojtaba Farjam farjam.md@gmail.com 200319475328460089650 200319475328460089650 No Noncommunicable Diseases Research Center, Fasa University of Medical Sciences, Fasa, Iran Mehdi Rezaeian dr.m.rezaian@gmail.com 200319475328460089651 200319475328460089651 No Noncommunicable Diseases Research Center, Fasa University of Medical Sciences, Fasa, Iran Fariba Asadi اسدی fariba.asadi3@gmail.com 200319475328460089652 200319475328460089652 No Department of Biostatistics, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran Fatemeh Masaebi مصائبی fatemeh.mb71@gmail.com 200319475328460089653 200319475328460089653 No Department of Biostatistics, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran Masoud Salehi salehi74@yahoo.com 200319475328460089654 200319475328460089654 No 5. Nutritional Sciences Research Center, Department of Biostatistics, School of Public Health, Iran University of Medical Sciences, Tehran, Iran Maryam Heydarpour Meymeh mmeymeh@yahoo.com 200319475328460089655 200319475328460089655 No Department of English Language, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran Farid Zayeri فرید f_zayeri@sbmu.ac.ir 200319475328460089656 200319475328460089656 Yes Proteomics Research Center and Department of Biostatistics, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran