Estimation of LDL-C using machine learning models and its comparison with directly measured and calculated LDL-C in Turkish pediatric population

Authors : Necla KOÇHAN

Pages : 63-75

Doi:10.47493/abantmedj.1217478

View : 101 | Download : 13

Publication Date : 2023-04-28

Article Type : Research Paper

Abstract :Objective: The assessment of lipid profiles in children is critical for the early detection of dyslipidemia. Low-density lipoprotein cholesterol insert ignore into journalissuearticles values(LDL-C); is one of the most often used measures in diagnosing and treating patients with dyslipidemia. Therefore, accurate determination of LDL-C levels is critical for managing lipid abnormalities. In this study, we aimed to compare various LDL-C estimating formulas with powerful machine-learning insert ignore into journalissuearticles values(ML); algorithms in a Turkish pediatric population. Materials and Methods: This study included 2,563 children under 18 who were treated at Sivas Cumhuriyet University Hospital in Sivas, Türkiye. LDL-C was measured directly using Roche direct assay and estimated using Friedewald\`s, Martin/Hopkins\`, Chen\`s, Anandaraja\`s, and Hattori\`s formulas, as well as ML predictive models insert ignore into journalissuearticles values(i.e., Ridge, Lasso, elastic net, support vector regression, random forest, gradient boosting and extreme gradient boosting);. The concordances between the estimates and direct measurements were assessed overall and separately for the LDL-C and TG sublevels. Linear regression analyses were also carried out, and residual error plots were created between each LDL-C estimation and direct measurement method. Results: The concordance was approximately 0.92-0.93 percent for ML models, and around 0.85 percent for LDL-C estimating formulas. The SVR formula generated the most concordant results insert ignore into journalissuearticles values(concordance=0.938);, while the Hattori and Martin-Hopkins formulas produced the least concordant results insert ignore into journalissuearticles values(concordance=0.851);. Conclusion: Since ML models produced more concordant LDL-C estimates compared to LDL-C estimating formulas, ML models can be used in place of traditional LDL-C estimating formulas and direct assays.
Keywords : Kardiyovasküler Hastalıklar, Kolesterol, Lipoproteinler, Düşük Yoğunluklu Lipoprotein, Makine Öğrenimi

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