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Abstract

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.

Keywords

Deep vein thrombosis Machine learning Treatment response prediction Clinical decision support

Article Details

Author Biography

Nan Zhou, M. Kandiah Faculty of Medicine and Health Science, Universiti Tunku Abdul Rahman, Kajang, Selangor 43000, Malaysia

Zhou Nan is currently at Tunku Abdul Rahman University in Malaysia, engaged in research on the application of machine learning in clinical practice.

How to Cite
Zhou, N., Ng, T. H., Foo, C. N., Ling, L., & Lim, Y. M. (2025). Machine learning model for predicting symptom improvement rates in hospitalized deep vein thrombosis patients. Future Technology, 5(1), 254–262. Retrieved from https://fupubco.com/futech/article/view/646
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