Scopus
YÖKSİS DOI Eşleşti
SJR Q1
A comparative evaluation of low-density lipoprotein cholesterol estimation: Machine learning algorithms versus various equations
Clinica Chimica Acta · Nisan 2024
YÖKSİS Kayıtları
A comparative evaluation of low-density lipoprotein cholesterol estimation: Machine learning algorithms versus various equations
Clinica Chimica Acta · 2024 SCI-Expanded
Dr. Öğr. Üyesi MUSLU KAZIM KÖREZ →
YÖKSİS Kayıtları — ISSN Eşleşmesi
Comparison of area under curves for serum methylglyoxal and glucose in patients with diabetes mellitus
2019 ISSN: 0009-8981 SCI-Expanded Q1
Prof. Dr. ALİ ÜNLÜ →
Determination of serum imatinib and its' metabolite in patients chronic myeloid leukemia
2019 ISSN: 0009-8981 SCI-Expanded Q1
Prof. Dr. SEDAT ABUŞOĞLU →
Analysis of phosphodiestrease inhibitors by liquid chromatography-tandem mass spectrometry method
2019 ISSN: 0009-8981 SCI-Expanded
Prof. Dr. ALİ ÜNLÜ →
Comparison of area under curves for serum methylglyoxal and glucose in patients with diabetes mellitus
2019 ISSN: 0009-8981 SCI-Expanded
Prof. Dr. ALİ ÜNLÜ →
A comparative evaluation of low-density lipoprotein cholesterol estimation: Machine learning algorithms versus various equations
2024 ISSN: 0009-8981 SCI-Expanded Q2
Dr. Öğr. Üyesi MUSLU KAZIM KÖREZ →
Makale Bilgileri
Dergi
Clinica Chimica Acta
ISSN00098981
Yayın TarihiNisan 2024
Cilt / Sayfa557
Scopus ID2-s2.0-85187723888
Özet
Background: Given the critical importance of Low-density lipoprotein cholesterol (LDL-C) levels in determining cardiovascular risk, it is essential to measure LDL-C accurately. Since the Friedewald formula generates incorrect predictions in many circumstances, new equations have been developed to overcome the Friedewald equations' shortcomings. This study aimed to compare estimated LDL-C with directly measured LDL-C (dLDL-C), as well as their performance in predicting LDL-C, utilizing Friedewald, extended Martin–Hopkins, Sampson, de Cordova, and Vujovic formulas and five machine learning (ML) algorithms. Methods: A total of 29,504 samples from the ISLAB-2 Core Laboratory were included in the study. All statistical analysis was performed using R version 4.1.2. Statistical Language. Results: Bayesian-Regularized Neural Network (BRNN) (r = 0.957) and Random Forest (RF) (r = 0.957) algorithms showed a higher correlation with dLDL-C than the other equations in all-testing dataset. All ML algorithms demonstrated less bias than pre-existing LDL-C equations with dLDL-C and outperformed the LDL-C estimation equations in terms of concordance in all-testing dataset. Conclusions: The results of our research indicate that when compared to conventional equations, ML algorithms are much more effective in predicting LDL-C. ML algorithms, aided by a vast dataset, could have the capability to predict LDL-C levels even in cases where triglyceride levels are high, unlike the limited usage of Friedewald formula.
Yazarlar (4)
1
Esra Paydas Hataysal
ORCID: 0000-0002-3538-8135
2
M. K. Korez
ORCID: 0000-0001-9524-6115
3
Fatih Yeşildal
4
Ferruh Kemal İşman
Anahtar Kelimeler
Bayesian-regularized neural network
Cardiovascular disease
Friedewald equation
Low-density lipoprotein cholesterol
Machine learning
Kurumlar
Goztepe Prof. Dr. Suleyman Yalcin City Hospital
Istanbul Turkey
Haydarpasa Numune Egitim ve Arastýrma Hastanesi
Istanbul Turkey
Selçuk Tip Fakültesi
Konya Turkey
Scimago Dergi (ISSN Eşleşmesi)
Clinica Chimica Acta
Q1
SJR Skoru0,935
H-Index182
YayıncıElsevier B.V.
ÜlkeNetherlands
Medicine (miscellaneous) (Q1)
Biochemistry (Q2)
Biochemistry (medical) (Q2)
Clinical Biochemistry (Q2)
Metrikler
7
Atıf
4
Yazar
5
Anahtar Kelime