Optimizing treatment to control LDL cholesterol using machine learning
Deiby Boneu Yepez1, David Sierra Porta2, Liz Morales Aguas3
1Ciencias básicas, programa de medicina, Corporación Universitaria Rafael Núñez, Cartagena, Bolívar, Colombia; Ciencias básicas, Maestría en estadística aplicada y ciencia de datos, Universidad Tecnológica de Bolívar, Cartagena, Bolívar, Colombia; Centro de Diagnostico Cardiológico SAS Cartagena, Bolívar, Colombia; TimeMed-IA Cartagena, Bolívar, Colombia.
Computers in Biology and Medicine
|June 21, 2025
Summary
Artificial intelligence models, specifically Random Forest Classifier, can optimize LDL cholesterol control in high-risk cardiovascular patients. These AI tools aid clinicians in selecting personalized therapies to effectively manage cardiovascular disease risk factors.
Area of Science:
- Cardiology
- Medical Informatics
- Artificial Intelligence
Background:
- Elevated LDL cholesterol is a primary risk factor for cardiovascular diseases.
- Effective therapy is crucial for reducing cardiovascular disease incidence.
- Artificial intelligence (AI) offers potential for personalized patient treatment selection.
Purpose of the Study:
- To identify the optimal artificial intelligence model for recommending individualized LDL-lowering therapy.
- To assess AI model performance in tailoring treatment based on patient cardiovascular risk factors.
Main Methods:
- Compared machine learning models including RandomForestClassifier, GradientBoostClassifier, and others.
- Evaluated model ability to predict optimal individualized therapy for cardiovascular patients.
- Utilized a dataset of 162 patients with cardiovascular risk and LDL alterations.
Main Results:
- Random Forest Classifier (RFC) and Gradient Boosting Classifier (GBC) showed superior performance.
- These models effectively classified optimal LDL-lowering therapy.
- Naïve Bayes Classifier (NBC) was found unsuitable due to outcome overestimation.
Conclusions:
- Machine learning models, especially RFC, are valuable for optimizing LDL control in high-risk cardiovascular patients.
- AI enhances clinical decision-making by enabling personalized therapy selection.
- AI facilitates individualized treatment strategies based on specific patient risk factors.
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