Türk Pediatrik Popülasyonunda Makine Öğrenimi Modelleri Kullanılarak LDL-K Tahmini ve Doğrudan Ölçülen ve Hesaplanan LDL-K ile Karşılaştırılması

Year 2023, , 63 - 75, 28.04.2023

Necla Koçhan

https://doi.org/10.47493/abantmedj.1217478

Abstract

Amaç: Çocuklarda lipid profillerinin değerlendirilmesi, dislipideminin erken saptanması için kritik öneme sahiptir. Düşük yoğunluklu lipoprotein kolesterol (LDL-K), dislipidemik hastaların teşhis ve tedavisinde en sık kullanılan ölçümlerden biridir. Bu nedenle, LDL-K düzeylerinin doğru belirlenmesi, lipid anormalliklerinin yönetimi için kritik öneme sahiptir. Bu çalışmada, Türk pediatrik popülasyonunda çeşitli LDL-K tahmin formüllerini güçlü makine öğrenmesi algoritmalarıyla karşılaştırmayı amaçladık.
Gereç ve Yöntemler: Bu çalışmaya Sivas Cumhuriyet Üniversitesi Hastanesi'nde tedavi gören 18 yaş altı 2,563 çocuk dahil edildi. LDL-K değerleri Roche direkt yöntemi kullanılarak ölçüldü ve Friedewald, Martin/Hopkins, Chen, Anandaraja ve Hattori formülleri ile makine öğrenmesi modelleri (Ridge, Lasso, elastic net, destek vektör regresyonu, rastgele orman, gradyan artırma ve aşırı gradyan artırma) kullanılarak tahmin edildi. Tahminler ve direkt ölçümler arasındaki uyum hem genel olarak hem de LDL-K ve TG alt seviyeleri için ayrı ayrı değerlendirildi. Ayrıca, doğrusal regresyon analizleri gerçekleştirilmiş olup her bir LDL-K tahmini ile direkt ölçüm yöntemi arasındaki fark artık hata grafikleri ile gösterilmiştir.
Bulgular: Tahminlenen LDL-K değerleri ile Roche direkt metodu ile ölçülen LDL-K değerleri arasındaki uyum, makine öğrenmesi modelleri için yaklaşık yüzde 0,92-0,93 ve LDL-K tahmin formülleri için yaklaşık %0,85 idi. Destek vektör regresyonu en uyumlu sonuçları (uyum=0,938) verirken, Hattori ve Martin-Hopkins formülleri en az uyumlu sonuçları (uyum=0,851) vermiştir.
Sonuç: Makine öğrenmesi modelleri, LDL-K tahmin formüllerine kıyasla daha uyumlu LDL-K tahmini yaptığından, makine öğrenmesi modelleri, geleneksel LDL-K tahmin formülleri ve doğrudan analizlerin yerine kullanılabilir.

Keywords

Kardiyovasküler Hastalıklar, Kolesterol, Lipoproteinler, Düşük-Yoğunluklu Lipoprotein, Makine Öğrenimi

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

Abstract

Objective: The assessment of lipid profiles in children is critical for the early detection of dyslipidemia. Low-density lipoprotein cholesterol (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 (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 (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 (concordance=0.938), while the Hattori and Martin-Hopkins formulas produced the least concordant results (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

Cardiovascular Diseases, Cholesterol, Lipoproteins, Low-Density Lipoprotein, Machine Learning

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