Integrated Machine Learning Decision Tree Model for Risk Evaluation in Patients with Non-Valvular Atrial Fibrillation When Taking Different Doses of Dabigatran

Yung-Chuan Huang, Yu-Chen Cheng, Mao-Jhen Jhou, Mingchih Chen and Chi-Jie Lu ()
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Yung-Chuan Huang: Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei City 242062, Taiwan
Yu-Chen Cheng: Department of Neurology, Fu Jen Catholic University Hospital, Fu Jen Catholic University, New Taipei City 24352, Taiwan
Mao-Jhen Jhou: Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei City 242062, Taiwan
Mingchih Chen: Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei City 242062, Taiwan
Chi-Jie Lu: Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei City 242062, Taiwan

IJERPH, 2023, vol. 20, issue 3, 1-15

Abstract: The new generation of nonvitamin K antagonists are broadly applied for stroke prevention due to their notable efficacy and safety. Our study aimed to develop a suggestive utilization of dabigatran through an integrated machine learning (ML) decision-tree model. Participants taking different doses of dabigatran in the Randomized Evaluation of Long-Term Anticoagulant Therapy trial were included in our analysis and defined as the 110 mg and 150 mg groups. The proposed scheme integrated ML methods, namely naive Bayes, random forest (RF), classification and regression tree (CART), and extreme gradient boosting (XGBoost), which were used to identify the essential variables for predicting vascular events in the 110 mg group and bleeding in the 150 mg group. RF (0.764 for 110 mg; 0.747 for 150 mg) and XGBoost (0.708 for 110 mg; 0.761 for 150 mg) had better area under the receiver operating characteristic curve (AUC) values than logistic regression (benchmark model; 0.683 for 110 mg; 0.739 for 150 mg). We then selected the top ten important variables as internal nodes of the CART decision tree. The two best CART models with ten important variables output tree-shaped rules for predicting vascular events in the 110 mg group and bleeding in the 150 mg group. Our model can be used to provide more visualized and interpretable suggestive rules to clinicians managing NVAF patients who are taking dabigatran.

Keywords: cardioembolic stroke; arrhythmia; anticoagulant agents; machine learning; decision tree (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2023
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