Computers in biology and medicine · 2025
Optimizing treatment to control LDL cholesterol using machine learning.
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Yepez Deiby Boneu, Porta David Sierra, Aguas Liz Morales, Jattin Fernando Manzur
Аннотация
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.
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.