Scopus
YÖKSİS Eşleşti
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
DOKTOR ÖĞRETİM ÜYESİ MUSLU KAZIM KÖREZ →
Makale Bilgileri
DergiClinica Chimica Acta
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