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

Abstract

Jheng-Ting Luo,1 Yung-Chun Hung,1,2 Gina Jinna Chen,3 Yu-Shiang Lin1 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 ChinaCorrespondence: 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 [email protected]: 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

Keywords

• bell’s palsy
• machine learning
• prognostic prediction
• feature ranking