Min Zhang
ABSTRACT
Hypertension is one of the most significant risk factors for atrial fibrillation (AF). However, few effective methods are available to support accurate prediction on the potential risk of atrial fibrillation among hypertensive patients currently. The aim of this paper is to illustrate a machine learning technology for constructing an atrial fibrillation intelligent prediction model. Eventually, the model can be employed to predict the risk of atrial fibrillation in hypertensive patients.
A total of 2,067 diagnosed hypertensive patients (including 721 hypertensive patients complicated with atrial fibrillation) by Heart Center of Affiliated Zhongshan Hospital of Dalian University from January 2015 to January 2018 were enrolled in this study. As result, the atrial fibrillation prediction model was constructed based on the C5.0 decision tree classification algorithm. Moreover, compared with other machine learning classification algorithms, C5.0 has similar performance to random forest (RF), but is better than support vector machine(SVM), Logical Regression(LR), CHAID, and K nearest neighbor(KNN) classification algorithms. The proposed predict model has high accuracy of atrial fibrillation risk prediction for hypertension patients.
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Index Terms
- Clinical Application of Intelligent Prediction Model for Atrial Fibrillation in Hypertensive Patients
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