Predicting synkinesis caused by Bell's palsy or Ramsay Hunt syndrome using machine learning‐based logistic regression

Abstract

Abstract Objective To investigate whether machine learning (ML)‐based algorithms, namely logistic regression (LR), random forest (RF), k‐nearest neighbor (k‐NN), and gradient‐boosting decision tree (GBDT), utilizing early post‐onset parameters can predict facial synkinesis resulting from Bell's palsy or Ramsay Hunt syndrome more accurately than the conventional statistics‐based LR. Methods This retrospective study included 362 patients who presented to a facial palsy outpatient clinic. Median follow‐up of synkinesis‐positive and ‐negative patients was 388 (range, 177–1922) and 198 (range, 190–3021) days, respectively. Electrophysiological examinations were performed, and the rate of synkinesis in Bell's palsy and Ramsay Hunt syndrome was evaluated. Sensitivity and specificity were assessed using statistics‐based LR; and electroneurography (ENoG) value, the difference in the nerve excitability test (NET), and scores of the subjective Yanagihara scaling system were evaluated using early post‐onset parameters with ML‐based LR, RF, k‐NN, and GBDT. Results Synkinesis rate in Bell's palsy and Ramsay Hunt syndrome was 20.2% (53/262) and 40.0% (40/100), respectively. Sensitivity and specificity obtained with statistics‐based LR were 0.796 and 0.806, respectively, and the area under the receiver operating characteristic curve (AUC) was 0.87. AUCs measured using ML‐based LR of “ENoG,” “difference in NET,” “Yanagihara,” and all three components (“all”) were 0.910, 0.834, 0.711, and 0.901, respectively. Conclusion ML‐based LR model shows potential in predicting facial synkinesis probability resulting from Bell's palsy or Ramsay Hunt syndrome and has comparable reliability to the conventional statistics‐based LR. Level of Evidence 3.

Keywords

• Bell's palsy
• machine learning
• Ramsay Hunt syndrome