Title : ( Association between serum hypertriglyceridemia and hematological indices: data mining approaches )
Authors: Amin Mansoori , Somayeh Ghiasi Hafezi , Alireza Kooshki , Marzieh Hosseini , Sahar Ghoflchi , Mark Ghamsary , Gordon Ferns , Habibollah Esmaily , Majid Ghayour-Mobarhan ,Access to full-text not allowed by authors
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.
Keywords
, Hypertriglyceridemia, Hematological factors, Machine learning, Random Forest, Decision Tree@article{paperid:1102342,
author = {Mansoori, Amin and سمیه غیاثی حافظی and علیرضا کوشکی and مرضیه حسینی and سحر قفلچی and مارک قمصری and گردون فرنس and حبیب الله اسماعیلی and مجید غیور مبرهن},
title = {Association between serum hypertriglyceridemia and hematological indices: data mining approaches},
journal = {BMC Medical Informatics and Decision Making},
year = {2024},
volume = {24},
number = {1},
month = {December},
issn = {1472-6947},
keywords = {Hypertriglyceridemia; Hematological factors; Machine learning; Random Forest; Decision Tree},
}
%0 Journal Article
%T Association between serum hypertriglyceridemia and hematological indices: data mining approaches
%A Mansoori, Amin
%A سمیه غیاثی حافظی
%A علیرضا کوشکی
%A مرضیه حسینی
%A سحر قفلچی
%A مارک قمصری
%A گردون فرنس
%A حبیب الله اسماعیلی
%A مجید غیور مبرهن
%J BMC Medical Informatics and Decision Making
%@ 1472-6947
%D 2024