Atrial fibrillation identification in patients with non-alcoholic fatty liver disease: a machine learning model based on immune-inflammatory markers

Key Result

The machine learning model effectively identified atrial fibrillation risk in patients with NAFLD, demonstrating excellent discriminatory ability.

Key Points

• This research aims to create a machine learning model to identify atrial fibrillation risk based on immune-inflammatory markers in patients with NAFLD.
• Developed a machine learning model using multiple immune inflammatory markers.
• Used a support vector machine (SVM) for model construction and validation.
• Created a web tool for practical use in identifying high-risk individuals.
• The SVM model demonstrated excellent ability to differentiate between individuals at risk and those not at risk of atrial fibrillation.
• The web tool effectively aids in identifying high-risk patients.
• The findings suggest new strategies for preventive interventions against atrial fibrillation.