Mostafa Boskabadi, Najmeh Mohajeri, Ali Taghipour, Habibollah Esmaily, Syeid Javad Hoseinij, Ehsan Mosa Farkhani,
Volume 22, Issue 6 (3-2023)
Abstract
Background: In Iran, with the advancement of technology and the development of registration statistics, the need to use data mining methods has attracted more attention from researchers. Regression and classification tree is one of the important methods in Big data modeling, which has attracted the attention of many researchers for community control and prediction. The purpose of this study is to determine the influencing variables on the occurrence of complications caused by diabetes.
Methods: This paper is a cross sectional-analytical study. In this research, all diabetic patients covered by Mashhad University of Medical Sciences in 2017 were extracted from the SINA system. The number of diabetics with complications was 5016 and diabetics without complications were 53613. The method of fitting the regression tree model and classification and measurement criteria of the model is the coefficient of determination and the area of the Rock curve and the Lift diagram.
Results: The rock curve for the fitted tree model is 73.8%, which shows the relatively high power of the model. Based on the Lift chart, the decision-making power of diabetes complications increases 3.5 times for the person who comes to visit.
Conclusion: The results of the regression model and tree classification showed that, in descending order, age, risk assessment factor, FBS, HbA1C, total activity time, cholesterol, FBS and HDL, cardiovascular disease, history of stroke, blood pressure, cholesterol Statin prescription, job with hard physical activity, living area, consumed oil, walking, consumption of vegetables and gender are more effective than other factors in the occurrence of diabetes complications.
Najmeh Seifi, Niloufar Abdollahpour, Gelare Koochakpoor, Nioosha Samadi, Habibollah Esmaily, Majid Ghayour-Mobarhan,
Volume 26, Issue 2 (7-2026)
Abstract
Background: Dietary acid load is thought to contribute to the development of metabolic syndrome through effects on acid–base balance with evidence being inconsistent. This study aimed to examine the associations between dietary acid load indices, Dietary Acid Load (DAL), Potential Renal Acid Load (PRAL), and Net Endogenous Acid Production (NEAP) and risk of metabolic syndrome and its components in an Iranian adult cohort.
Methods: 6,482 adults aged 35-65 years were enrolled from the Mashhad Stroke and Atherosclerosis Study. Dietary intake was assessed using a validated food frequency questionnaire, and dietary acid load indices were calculated. Participants were followed for nearly 10 years. Logistic regression models were used to evaluate associations of DAL, PRAL, and NEAP with the incidence of metabolic syndrome, diabetes, hypertension, and dyslipidemia, adjusting for major confounders.
Results: Following adjustment, no significant associations were found between DAL indices and the incidence of metabolic syndrome or its components. DAL odds ratio (OR) for the highest versus lowest tertile was 0.984 [95% confidence interval (CI): 0.815–1.188; p= 0.868]. For PRAL, the OR was 1.024 (95% CI: 0.865–1.212; p= 0.782), and for NEAP, the OR was 0.902 (95% CI: 0.763–1.066; p= 0.227). None of the indices were significantly related to new cases of diabetes, hypertension, or dyslipidemia (all p> 0.05).
Conclusion: In this Iranian adult cohort, DAL indices were not associated with the risk of metabolic syndrome or its components. Further studies in populations with different dietary habits are needed to clarify potential threshold effects or effect modifiers.
