Predictive modeling of maneuver numbers in BPPV therapy using machine learning

Abstract

Objective

Some patients with benign paroxysmal positional vertigo (BPPV) do not improve with a single maneuver and may require multiple maneuvers. This study aims to utilize machine learning (ML) to identify parameters predisposing multiple CRMs, thus enhancing the predictability of treatment requirements in BPPV patients.

Study design

Retrospective study.

Setting

Hospital.

Patients

This study included 520 participants diagnosed with BPPV between 2018 and 2023, with a mean age of 56.2 ± 14.0 years.

Interventions

Age, BPPV type, comorbid diseases, gender, and number of maneuvers that the patients recovered with were determined. The target outcome—“number of maneuvers”—was dichotomized as either one (0) or more than one (1). The models’ success was evaluated using metrics such as precision, F1-score, accuracy, balanced accuracy, recall, area under the Receiver Operating Characteristic (ROC), and area under the curve (AUC).

Results

The applied maneuver number to treat BPPV was 188 (36%) in one maneuver and 332 (67%) in more than one maneuvers. Gradient Boosting Machine (GBM) had the best AUC in maneuver number estimation. Also, logistic regression resulted the best precision score; XGBoost showed the best F1 and recall score while support vector classifier showed the best accuracy and balanced accuracy scores.

Conclusions

Machine learning models with high predictive capabilities can help identify patients likely to need multiple maneuvers, allowing for more efficient treatment planning and enhanced patient outcomes.