Comparison between statistical and machine learning methods to detect the hematological indices with the greatest influence on elevated serum levels of low-density lipoprotein cholesterol

Elevated levels of low-density lipoprotein-cholesterol (LDL-C) is a significant risk factor for the development of cardiovascular diseases (CVD)s. Furthermore, studies have revealed an association between indices of the complete blood count (CBC) and dyslipidemia. We aimed to investigate the relatio...

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Published in:Chemistry and physics of lipids 2024-11, Vol.265, p.105446, Article 105446
Main Authors: Ghiasi Hafezi, Somayeh, Behkamal, Bahareh, Rashidmayvan, Mohammad, Hosseini, Marzieh, Yadegari, Mehran, Ghoflchi, Sahar, Mansoori, Amin, Ghamsary, Mark, Ferns, Gordon, Saberi, Mohammad Reza, Esmaily, Habibollah, Ghayour-Mobarhan, Majid
Format: Article
Language:English
Subjects:
Adult
Aged
biomarkers
Blood Cell Count
blood platelet count
blood serum
Cardiovascular disease (CVD)
chemistry
Cholesterol, LDL - blood
decision support systems
Decision tree
erythrocyte count
Female
hematocrit
hemoglobin
Humans
hyperlipidemia
low density lipoprotein cholesterol
Low-density lipoprotein (LDL)
Machine Learning
Male
Middle Aged
Neural network
physics
Prospective Studies
Random forest
regression analysis
risk factors
Support vector machine
support vector machines
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Summary:Elevated levels of low-density lipoprotein-cholesterol (LDL-C) is a significant risk factor for the development of cardiovascular diseases (CVD)s. Furthermore, studies have revealed an association between indices of the complete blood count (CBC) and dyslipidemia. We aimed to investigate the relationship between CBC parameters and serum levels of LDL. In a prospective study involving 9704 participants aged 35–65 years, comprehensive screening was conducted to estimate LDL-C levels and CBC indicators. The association between these biomarkers and high LDL-C (LDL-C≥130 mg/dL (3.25 mmol/L)) was investigated using various analytical methods, including Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Neural Network (NN), and Support Vector Machine (SVM) methodologies. The present study found that age, hemoglobin (HGB), hematocrit (HCT), platelet count (PLT), lymphocyte (LYM), PLT-LYM ratio (PLR), PLT-High-Density Lipoprotein (HDL) ratio (PHR), HGB-LYM ratio (HLR), red blood cell count (RBC), Neutrophil-HDL ratio (NHR), and PLT-RBC ratio (PRR) were all statistically significant between the two groups (p
ISSN:0009-3084
1873-2941
1873-2941
DOI:10.1016/j.chemphyslip.2024.105446