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

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.

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.

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.

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.

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