Prediction of LDL in hypertriglyceridemic subjects using an innovative ensemble machine learning technique

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Demirci F., Emec M., Gursoy Doruk O., Örmen M., Akan P., Hilal Ozcanhan M. H.

TURKISH JOURNAL OF BIOCHEMISTRY, vol.0, no.0, pp.1-12, 2023 (SCI-Expanded)

  • Publication Type: Article / Article
  • Volume: 0 Issue: 0
  • Publication Date: 2023
  • Doi Number: 10.1515/tjb-2023-0154
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Food Science & Technology Abstracts, Directory of Open Access Journals
  • Page Numbers: pp.1-12
  • Dokuz Eylül University Affiliated: Yes


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.