Atıf İçin Kopyala
Demirci F., Emec M., Gursoy Doruk O., Örmen M., Akan P., Hilal Ozcanhan M. H.
TURKISH JOURNAL OF BIOCHEMISTRY, cilt.0, sa.0, ss.1-12, 2023 (SCI-Expanded)
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Yayın Türü:
Makale / Tam Makale
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Cilt numarası:
0
Sayı:
0
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Basım Tarihi:
2023
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Doi Numarası:
10.1515/tjb-2023-0154
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Dergi Adı:
TURKISH JOURNAL OF BIOCHEMISTRY
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Derginin Tarandığı İndeksler:
Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Food Science & Technology Abstracts, Directory of Open Access Journals
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Sayfa Sayıları:
ss.1-12
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Dokuz Eylül Üniversitesi Adresli:
Evet
Özet
Abstract
Objectives
Determining low-density lipoprotein (LDL) is a costly and time-consuming operation, but triglyceride value above 400 (TG>400) always requires LDL measurement. Obtaining a fast LDL forecast by accurate prediction can be valuable to experts. However, if a high error margin exists, LDL prediction can be critical and unusable. Our objective is LDL value and level prediction with an error less than low total acceptable error rate (% TEa).
Methods
Our present work used 6392 lab records to predict the patient LDL value using state-of-the-art Artificial Intelligence methods. The designed model, p-LDL-M, predicts LDL value and class with an overall average test score of 98.70 %, using custom, hyper-parameter-tuned Ensemble Machine Learning algorithm.
Results
The results show that using our innovative p-LDL-M is advisable for subjects with critical TG>400. Analysis proved that our model is positively affected by the Hopkins and Friedewald equations normally used for (TG≤400). The conclusion follows that the test score performance of p-LDL-M using only (TG>400) is 7.72 % inferior to the same p-LDL-M, using Hopkins and Friedewald supported data. In addition, the test score performance of the NIH-Equ-2 for (TG>400) is much inferior to p-LDL-M prediction results.
Conclusions
In conclusion, obtaining an accurate and fast LDL value and level forecast for people with (TG>400) using our innovative p-LDL-M is highly recommendable.