Machine Learning Predictions and Identifying Key Predictors for Safer Intubation: A Study on Video Laryngoscopy Views

Abstract

This study develops a predictive model for video laryngoscopic views using advanced machine learning techniques, aiming to enhance airway management’s efficiency and safety. A total of 212 participants were involved, with 169 in the training set and 43 in the test set. We assessed outcomes using the percentage of glottic opening (POGO) score and considered factors like the modified Mallampati classification, thyromental height and distance, sternomental distance, mouth opening distance, and neck circumference. A range of machine learning algorithms was employed for data analysis, including Random Forest, Light Gradient Boosting Machine, K-Nearest Neighbors, Support Vector Regression, Ridge Regression, and Lasso Regression. The models’ performance was evaluated on the test set, with Root Mean Squared Error values ranging from 20.4 to 21.9. SHapley Additive exPlanations value analysis revealed that age is a consistent and significant predictor of POGO score across all models, highlighting its critical role in the predictive accuracy of these techniques.