Machine learning model for predicting symptom improvement rates in hospitalized deep vein thrombosis patients

Deep Vein Thrombosis (DVT) demonstrates considerable treatment response heterogeneity, with 40-60% of patients developing complications despite standard anticoagulation therapy. Accurate prediction of individual treatment outcomes remains an unmet clinical need. This study develops and validates a machine learning-based model to predict symptom Improvement Rate (IPR) using retrospective data from 403 hospitalized DVT patients (2018-2023). Six predictive features are identified using Random Forest-based Recursive Feature Elimination (RFE): age, white blood cell count, Activated Partial Thromboplastin Time (APTT), Thrombin Time (TT), surgical intervention status, and baseline symptom severity. The regularized eXtreme Gradient Boosting (XGBoost) algorithm achieves optimal performance with a test coefficient of determination (R²) of 0.60, Root Mean Square Error (RMSE) of 12.36, and five-fold cross-validation R² of 0.58 ± 0.07. SHapley Additive exPlanations (SHAP) analysis reveals that APTT and surgical intervention are the strongest predictors of treatment response. The validated model is deployed as a publicly accessible web-based clinical decision support tool, enabling real-time outcome prediction at the point of care. This research establishes a practical framework bridging predictive analytics and clinical practice, facilitating evidence-based, personalized DVT management strategies.