Machine learning-based models for predicting warfarin maintenance dose and investigating factors affecting INR status in patients with cardiovascular diseases: a cross-sectional study

Warfarin is a commonly prescribed anticoagulant, and its dosing requires careful monitoring. This study used machine learning-based models to predict optimal warfarin dosage and identify factors influencing the International Normalized Ratio (INR) status in patients with cardiovascular diseases (CVDs). This study involved 490 patients with CVDs on warfarin therapy at a hospital. Data were collected from patient records and direct interviews using a questionnaire. The various machine learning (ML) algorithms, including Support Vector Machine (SVM), Multinomial Logistic Regression (MLR), Random Forest (RF), Decision Tree (DT), and Ensemble Models, predict the optimal warfarin dosage for patients. Data were balanced using the Synthetic Minority Over-sampling Technique (SMOTE) algorithm to address class imbalances, and model performance was evaluated using metrics such as Precision, F1 Score, Accuracy, and Area Under the ROC Curve (AUC), with all analyses performed in Python using Jupyter Notebook. After balancing the data using SMOTE, ML algorithms were evaluated, with RF and SVM achieving similar accuracy of 75.7% and AUC values of 94% for RF and 93% for SVM. The ensemble (RF, MLR) and ensemble (SVM, MLR, RF) models outperformed others, achieving an accuracy of 76.4%, sensitivity of 75.2%, specificity of 92.1%, and AUC values of 95% and 94%, respectively. The DT model showed the lowest performance with an accuracy of 67.8%, an AUC of 79%, and a precision of 69%. Feature importance analysis indicated that INR, BMI, and warfarin indications were the most influential factors in predicting warfarin dosage, while gender, amiodarone, and nationality had minimal impact. Ensemble ML algorithms, particularly those combining RF and SVM, show strong potential for accurately predicting warfarin dosages in cardiovascular patients. Key predictors such as INR, BMI, and warfarin indication improved model accuracy, while less impactful factors included gender and amiodarone use, supporting RF-SVM ensembles as effective tools for personalized dosing.