Optimizing treatment to control LDL cholesterol using machine learning.

INTRODUCTION

Increased LDL cholesterol is one of the main risk factors for cardiovascular diseases; therefore, adequate therapy reduces the risk of developing cardiovascular disease. Artificial intelligence (AI) is a tool that can significantly help doctors select the optimal treatment aimed at each patient individually. Based on the above, the question arises: Which artificial intelligence model allows us to recommend the best treatment to control LDL levels according to the patient's cardiovascular risk factor?

METHODOLOGY: The performance of various machine learning models was compared to assess their ability to predict the best-individualized therapy for each patient according to their cardiovascular risk from a registry of patients at a clinic specializing in cardiovascular diseases. The Machine learning models used included: RandomForestClassifier (RFC), GradientBoostClassifier (GBC), AdaBoostClassifier (ABC), ExtraTreeClassifier (ETC), Decision Tree Classifier (DTC), KNN, Support Vector Machine (SVM), Multinomial Logistic Regression (MLR), and Naive Bayes Classifier (NBC).

POPULATION AND SAMPLE: The records of 166 patients with any cardiovascular risk who had LDL alterations and used some therapeutic interventions were obtained. However, four medical records did not have creatinine levels; therefore, they were excluded, counting at the end with 162 observations. Of these, a sample of 115 patients who achieved the LDL therapeutic goal was obtained.

RESULTS

The Random Forest Classifier (RFC) and Gradient Boosting Classifier (GBC) demonstrated superior performance in classifying optimal LDL-lowering therapy. In contrast, Naïve Bayes Classifier (NBC) overestimated outcomes and was deemed unsuitable.

CONCLUSIONS

Machine learning models, particularly Random Forest Classifier, provide valuable tools for optimizing LDL control in high-risk cardiovascular patients. These models enhance clinical decision-making by enabling personalized therapy selection based on patient-specific risk factors.