Predicting Early Treatment Effectiveness in Bell’s Palsy Using Machine Learning - A Focus on Corticosteroids and Antivirals

Aug 4, 2026

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Bell’s palsy is a rapid-onset unilateral facial weakness or paralysis with a meaningful proportion of patients showing poor recovery despite many improving within months. This study applied six machine learning models to predict outcomes of early therapy using Scotland hospital data. Patients received prednisolone, acyclovir, both, or placebo, and recovery was evaluated with the House-Brackmann scale at 3 and 9 months. Logistic regression achieved the best AUC performance, and feature ranking highlighted age and prednisolone as key predictors.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages12

Reading Time30 min

AccessFree to download

Introduction

Study purpose and methods

Patients and outcomes

Machine learning models and evaluation

Results and feature ranking

Conclusion

International Journal of General Medicine downloaded from [https://www.dovepress.com/](https://www.dovepress.com/) For personal use only. International Journal of General Medicine Dovepress Open Access Full Text Article ORIGINAL RESEARCH Predicting Early Treatment Effectiveness in Bell’s Palsy Using Machine Learning: A Focus on Corticosteroids and Antivirals Jheng-Ting Luo 1 , Yung-Chun Hung 1 , 2 , Gina Jinna Chen 3 , Yu-Shiang Lin 1 1In-Service Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan; 2School of Health Care Administration, College of Management, Taipei Medical University, Taipei, Taiwan; 3Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen, People’s Republic of China Correspondence: Yu-Shiang Lin, In-Service Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, No. 250, Wuxing St., Xinyi District, Taipei City, 11031, Taiwan, Email [eriklin@tmu.edu.tw](eriklin@tmu.edu.tw) | Purpose: Facial nerve paralysis, particularly Bell’s palsy, manifests as a rapid onset of unilateral facial weakness or paralysis. Despite most patients recovering within three to six months, a significant proportion experience poor recovery. This study utilized six machine learning models to investigate the effectiveness of early treatment in Bell’s palsy.
Patients and Methods: We applied data from 17 hospitals in Scotland to predict treatment outcomes. Patients were randomized into four groups: Prednisolone (corticosteroids), Acyclovir (antivirals), both, and placebo. Outcomes, defined as full resolution of symptoms, were assessed using the House-Brackmann scale at 3 and 9 months post-treatment. We employed six different machine learning models to predict recovery outcomes and evaluated model performance using AUC, precision, recall, and F1-score. Results: Among 493 patients, 72.6% recovered after three months and 89.5% after nine months. Logistic regression demonstrated the highest predictive performance for both 3-month (AUC = 0.751) and 9-month recovery (AUC = 0.720) . Additionally, several models achieved Precision levels exceeding 0.9. We further employed the best-performing logistic regression for feature ranking, indicating that the patient’s age and prednisolone administration are the most significant predictors of recovery.
Conclusion: The results highlight the potential of machine learning models in predicting the effectiveness of early treatment. This study conducted a comprehensive comparison of six different machine learning models, with the logistic regression showing the highest predictive performance for both 3-month and 9-month recovery. Additionally, feature ranking using logistic regression supported the importance of Prednisolone in treatment. Notably, our findings revealed the significance of age in prognosis evaluation for the first time. This suggests that future research should further develop age-specific prognostic models, enabling clinicians to tailor individualized treatment strategies more effectively. This previously unrecognized discovery provides a foundation for prognostic analysis in Bell’s palsy patients.
Keywords: Bell’s palsy, machine learning, prognostic prediction, feature ranking | | --- | | Introduction
Facial nerve paralysis is the loss of facial movement due to pathological changes in the facial nerve, leading to impaired function of the voluntary facial muscles innervated by the nerve, resulting in facial asymmetry.1 Bell’s palsy is the most common peripheral paralysis of the seventh cranial nerve. This idiopathic condition has a rapid onset.2 Bell’s palsy should be suspected in patients with acute onset of unilateral facial weakness or paralysis involving the forehead in the absence of other neurologic abnormalities.3 The annual incidence is 15 to 20 per 100,000, with 40,000 new cases yearly. The lif

FAQs

How were treatment groups and outcomes defined in the study?

Patients were randomized into prednisolone, acyclovir, both, and placebo groups. Outcomes were defined as full symptom resolution and assessed using the House-Brackmann scale at 3 and 9 months.

Which machine learning model showed the best predictive performance?

Logistic regression demonstrated the highest predictive performance for recovery at both 3 months (AUC = 0.751) and 9 months (AUC = 0.720).

What predictors were identified as most important for recovery?

Feature ranking using logistic regression indicated patient age and prednisolone administration as the most significant predictors of recovery.

Aug 4, 2026

Upload by Liam