Important Risk Factors in Patients with Nonvalvular Atrial Fibrillation Taking Dabigatran Using Integrated Machine Learning Scheme—A Post Hoc Analysis

Huang, Yung-Chuan; Cheng, Yu-Chen; Jhou, Mao-Jhen; Chen, Mingchih; Lu, Chi-Jie

doi:10.3390/jpm12050756

Open AccessArticle

Important Risk Factors in Patients with Nonvalvular Atrial Fibrillation Taking Dabigatran Using Integrated Machine Learning Scheme—A Post Hoc Analysis

by

Yung-Chuan Huang

^1,2

,

Yu-Chen Cheng

²,

Mao-Jhen Jhou

¹,

Mingchih Chen

^1,3

and

Chi-Jie Lu

^1,3,4,*

¹

Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei City 242062, Taiwan

²

Department of Neurology, Fu Jen Catholic University Hospital, Fu Jen Catholic University, New Taipei City 24352, Taiwan

³

Artificial Intelligence Development Center, Fu Jen Catholic University, New Taipei City 242062, Taiwan

⁴

Department of Information Management, Fu Jen Catholic University, New Taipei City 242062, Taiwan

^*

Author to whom correspondence should be addressed.

J. Pers. Med. 2022, 12(5), 756; https://doi.org/10.3390/jpm12050756

Submission received: 7 April 2022 / Revised: 29 April 2022 / Accepted: 3 May 2022 / Published: 6 May 2022

(This article belongs to the Special Issue Big Data Analysis in Personalized Medicine)

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Abstract

Our study aims to develop an effective integrated machine learning (ML) scheme to predict vascular events and bleeding in patients with nonvalvular atrial fibrillation taking dabigatran and identify important risk factors. This study is a post-hoc analysis from the Randomized Evaluation of Long-Term Anticoagulant Therapy trial database. One traditional prediction method, logistic regression (LGR), and four ML techniques—naive Bayes, random forest (RF), classification and regression tree, and extreme gradient boosting (XGBoost)—were combined to construct our scheme. Area under the receiver operating characteristic curve (AUC) of RF (0.780) and XGBoost (0.717) was higher than that of LGR (0.674) in predicting vascular events. In predicting bleeding, AUC of RF (0.684) and XGBoost (0.618) showed higher values than those generated by LGR (0.605). Our integrated ML feature selection scheme based on the two convincing prediction techniques identified age, history of congestive heart failure and myocardial infarction, smoking, kidney function, and body mass index as major variables of vascular events; age, kidney function, smoking, bleeding history, concomitant use of specific drugs, and dabigatran dosage as major variables of bleeding. ML is an effective data analysis algorithm for solving complex medical data. Our results may provide preliminary direction for precision medicine.

Keywords: arrhythmia; cardioembolic stroke; non-vitamin K antagonist oral anticoagulants; dabigatran; machine learning

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MDPI and ACS Style

Huang, Y.-C.; Cheng, Y.-C.; Jhou, M.-J.; Chen, M.; Lu, C.-J. Important Risk Factors in Patients with Nonvalvular Atrial Fibrillation Taking Dabigatran Using Integrated Machine Learning Scheme—A Post Hoc Analysis. J. Pers. Med. 2022, 12, 756. https://doi.org/10.3390/jpm12050756

AMA Style

Huang Y-C, Cheng Y-C, Jhou M-J, Chen M, Lu C-J. Important Risk Factors in Patients with Nonvalvular Atrial Fibrillation Taking Dabigatran Using Integrated Machine Learning Scheme—A Post Hoc Analysis. Journal of Personalized Medicine. 2022; 12(5):756. https://doi.org/10.3390/jpm12050756

Chicago/Turabian Style

Huang, Yung-Chuan, Yu-Chen Cheng, Mao-Jhen Jhou, Mingchih Chen, and Chi-Jie Lu. 2022. "Important Risk Factors in Patients with Nonvalvular Atrial Fibrillation Taking Dabigatran Using Integrated Machine Learning Scheme—A Post Hoc Analysis" Journal of Personalized Medicine 12, no. 5: 756. https://doi.org/10.3390/jpm12050756

APA Style

Huang, Y.-C., Cheng, Y.-C., Jhou, M.-J., Chen, M., & Lu, C.-J. (2022). Important Risk Factors in Patients with Nonvalvular Atrial Fibrillation Taking Dabigatran Using Integrated Machine Learning Scheme—A Post Hoc Analysis. Journal of Personalized Medicine, 12(5), 756. https://doi.org/10.3390/jpm12050756

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Important Risk Factors in Patients with Nonvalvular Atrial Fibrillation Taking Dabigatran Using Integrated Machine Learning Scheme—A Post Hoc Analysis

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