Related Experiment Video
Updated: Jun 21, 2025
LDL Cholesterol Uptake Assay Using Live Cell Imaging Analysis with Cell Health Monitoring
Published on: November 17, 2018
Explainable artificial intelligence for LDL cholesterol prediction and classification
Sevilay Sezer1, Ali Oter2, Betul Ersoz3
1Department of Medical Biochemistry, Ministry of Health, Ankara Bilkent City Hospital, Ankara, Turkey.
Insights
Artificial Intelligence (AI) accurately predicts low-density lipoprotein cholesterol (LDL-C) levels, outperforming traditional formulas. Explainable AI (XAI) ensures these predictions are interpretable, enhancing clinical decision-making for atherosclerotic heart disease risk.
Area of Science:
- Cardiovascular Medicine
- Biomedical Informatics
- Artificial Intelligence in Healthcare
Background:
- Low-density lipoprotein cholesterol (LDL-C) monitoring is crucial for managing atherosclerotic heart disease risk.
- Accurate LDL-C measurement or estimation is vital in clinical practice.
- Existing methods for LDL-C calculation may have limitations in precision.
Purpose of the Study:
- To evaluate Artificial Intelligence (AI) and Explainable AI (XAI) for predicting LDL-C levels.
- To compare AI-driven LDL-C predictions with traditional calculated values.
- To emphasize the interpretability of AI models in LDL-C estimation.
Main Methods:
- Retrospective analysis of 60,217 patient lipid profiles from a hospital Laboratory Information System.
- Application of AI models including Gradient Boosting (GB), Random Forests (RF), Support Vector Machines (SVM), and Decision Trees (DT).
- Utilized XAI techniques (SHAP, LIME) to interpret AI model predictions and compared with direct LDL-C measurements and formula-based calculations.
Main Results:
- AI models, particularly RF and GB, demonstrated a stronger correlation with directly measured LDL-C than formula-based calculations.
- Total Cholesterol (TC) was identified as the most significant predictor of LDL-C using SHAP and LIME.
- AI-based LDL-C classification showed higher agreement with NCEP ATPIII guidelines compared to formula-based methods.
Conclusions:
- AI offers a reliable and explainable approach for estimating and classifying LDL-C levels.
- AI-driven predictions enhance the accuracy of LDL-C assessment in clinical settings.
- The interpretability of AI models (XAI) is key to their clinical adoption for cardiovascular risk management.
Introduction:
Monitoring LDL-C levels is essential in clinical practice because there is a direct relation between low-density lipoprotein cholesterol (LDL-C) levels and atherosclerotic heart disease risk. Therefore, measurement or estimate of LDL-C is critical. The present study aims to evaluate Artificial Intelligence (AI) and Explainable AI (XAI) methodologies in predicting LDL-C levels while emphasizing the interpretability of these predictions.
Materials And Methods:
We retrospectively reviewed data from the Laboratory Information System (LIS) of Ankara Etlik City Hospital (AECH). We included 60.217 patients with standard lipid profiles (total cholesterol [TC], high-density lipoprotein cholesterol, and triglycerides) paired with same-day direct LDL-C results. AI methodologies, such as Gradient Boosting (GB), Random Forests (RF), Support Vector Machines (SVM), and Decision Trees (DT), were used to predict LDL-C and compared directly measured and calculated LDL-C with formulas. XAI techniques such as Shapley additive annotation (SHAP) and locally interpretable model-agnostic explanation (LIME) were used to interpret AI models and improve their explainability.
Results:
Predicted LDL-C values using AI, especially RF or GB, showed a stronger correlation with direct measurement LDL-C values than calculated LDL-C values with formulas. TC was shown to be the most influential factor in LDL-C prediction using SHAP and LIME. The agreement between the treatment groups based on NCEP ATPIII guidelines according to measured LDL-C and the LDL-C groups obtained with AI was higher than that obtained with formulas.
Conclusions:
It can be concluded that AI is not only a reliable method but also an explainable method for LDL-C estimation and classification.
More Related Videos
Related Concept Videos
01:29Cholesterol: Significance and Regulation
Considering cholesterol and...
01:20Lipid-Lowering Drugs: Statins and Miscellaneous Agents
01:25Blood Studies for Cardiovascular System III: Serum Lipid Profile
Serum lipids are fats and fatty substances in the blood and are crucial for various bodily functions, including energy storage, cellular structure, and hormone production. Serum lipids consist of cholesterol, triglycerides, and phospholipids.
Cholesterol is a soft, fat-like substance found in all body cells. It is crucial for producing hormones, vitamin D, and substances that aid...
01:38Inflammation