Predictive Modeling of Deep Vein Thrombosis Risk in Hospitalized Patients: A Q-Learning Enhanced Feature Selection Model

Background Deep vein thrombosis (DVT) represents a critical health concern due to its potential to lead to pulmonary embolism, a life-threatening complication. Early identification and prediction of DVT are crucial to prevent thromboembolic events and implement timely prophylactic measures in high-risk individuals. Objective The study aims to examine the risk determinants associated with acute lower extremity DVT in hospitalized individuals. Additionally, it introduces an innovative approach by integrating Q-learning augmented colony predation search ant colony optimizer (QL-CPSACO) into the analysis. This algorithm, when combined with support vector machines (SVM), forms a bQL-CPSACO-SVM feature selection model dedicated to crafting a clinical risk prognostication model for DVT. Method The methodology involves the amalgamation of QL-CPSACO with SVM to create the bQL-CPSACO-SVM model. This model is specifically designed for feature selection in the DVT risk prognostication. The effectiveness of the algorithm's optimization and the model's accuracy are assessed through experiments utilizing the CEC 2017 benchmark functions and predictive analyses on the DVT dataset. Experimental Result The findings reveal that the proposed model achieves an outstanding accuracy of 95.90% in predicting DVT. Key parameters such as D-dimer, normal plasma prothrombin time, prothrombin percentage activity, age, previously documented DVT, leukocyte count, and thrombocyte count demonstrate significant value in the prognostication of DVT. Conclusion The proposed bQL-CPSACO-SVM model, which integrates QL-CPSACO with SVM, not only offers a rapid and straightforward approach but also accurately predicts a patient's risk of developing DVT. This method provides a basis for risk assessment at the time of patient admission and offers substantial guidance to physicians in making therapeutic decisions.