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Association Between Serum Hypertriglyceridemia and Hematological Indices: Data Mining Approaches Publisher Pubmed



Ghiasi Hafezi S1, 2 ; Mansoori A2 ; Kooshki A3 ; Hosseini M4 ; Ghoflchi S5 ; Ghamsary M6 ; Ferns G7 ; Esmaily H1, 8 ; Ghayourmobarhan M5
Authors
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Authors Affiliations
  1. 1. Department of Biostatistics, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran
  2. 2. Department of Applied Mathematics, School of Mathematical Sciences, Ferdowsi University of Mashhad, Mashhad, Iran
  3. 3. Student Research Committee, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
  4. 4. Department of Biostatistics, College of Health, Isfahan University of Medical Sciences, Isfahan, Iran
  5. 5. Department of Nutrition, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
  6. 6. School of Public Health, Loma Linda University, Loma Linda, CA, United States
  7. 7. Brighton and Sussex Medical School, Division of Medical Education, Brighton, United Kingdom
  8. 8. Social Determinants of Health Research Center, Mashhad University of Medical Sciences, Mashhad, Iran

Source: BMC Medical Informatics and Decision Making Published:2024


Abstract

Background: High triglyceride (TG) affects and is affected of other hematological factors. The determination of serum fasted triglycerides concentrations, as part of a lipid profile, is crucial key point in hematological factors and significantly affect various systemic diseases. This study was carried out to assess the potential relation between the concentration of TG and hematological factors. Method: Our sample size was 9704 participants beginning in 2007 and ending in 2020 aged between 35 and 65 years, sourced from the MASHAD cohort (northeastern Iran). Machine learning methodologies, specifically logistic regression, decision tree, and random forest algorithms, were utilized for data analysis in the investigation of individuals with normal and high TG levels. Results: The highest Gini score belongs to RLR (Red cell distribution width/Lymphocyte) (236.10), RPR (Red cell distribution width/Platelets) (215.78), and PHR (Platelets/high-density lipoprotein) (273.66). We also found that factors such as age are statistically associated with the level of TG in women probably due to the drop in menopausal estrogen. RF model showed to have higher accuracy in predicting the TG level in both males and females. Conclusion: Our model assessed the association between serum TG with several hematological factors like RLR, RPR, and PHR. Other hematological factors also have been reported to be related to the TG level. As these results give us new insights into the association of TG on various hematological factors and their possible interactions with each other. future studies are needed to provide sufficient data for the mechanism and the pathophysiology of the findings. © The Author(s) 2024.
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