Integrated Machine Learning Decision Tree Model for Risk Evaluation in Patients with Non-Valvular Atrial Fibrillation When Taking Different Doses of Dabigatran
Bibliographic Data
| ID | 15480908 |
|---|---|
| Authors | Yung-Chuan Huang (0000-0001-5171-2911, Fu Jen Catholic University), Yu‐Chen Cheng (0009-0003-2938-4933, Fu Jen Catholic University), Yu-Chen Cheng (0009-0001-9029-1053, Department of Neurology, Fu Jen Catholic University Hospital, Fu Jen Catholic University, New Taipei City 24352, Taiwan), Mao-Jhen Jhou (0000-0003-1250-7434, Fu Jen Catholic University), Mingchih Chen (0000-0002-8278-0033, Fu Jen Catholic University), Chi-Jie Lu (0000-0002-7911-2253, Fu Jen Catholic University, corresponding author) |
| Year | 2023 |
| Volume | 20 |
| Issue | 3 |
| Pages | 2359-2359 |
| Publication date | 2023-01-29 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | International Journal of Environmental Research and Public Health (JOURNAL) |
| Journal identifiers | ISSN: 1661-7827 • E-ISSN: 1660-4601 |
| Publisher | Multidisciplinary Digital Publishing Institute (PUBLISHER • CH) |
| DOI | 10.3390/ijerph20032359 |
| PMID | 36767726 |
| OpenAlex | W4318474917 |
| Language | EN |
| References cited | 45 |
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
Atrial fibrillation · Boosting (machine learning · Cart · Dabigatran · Decision tree · Gradient boosting · Logistic regression · Machine learning · Naive Bayes classifier · Random forest · Stroke (engine · Support vector machine · Tree (set theory · Warfarin · Atrial Fibrillation Management and Outcomes · Computational Drug Discovery Methods · Computer Science · Mathematics · Medicine · Artificial Intelligence · Internal Medicine
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| Citation velocity | historical |
|---|---|
| Highly cited | No |