Machine-learning-based analytics for risk forecasting of anaphylaxis during general anesthesia

Abstract

Perioperative anaphylaxis has a risk of mortality and compromised quality of patient care. It is difficult to design an evaluation system for risk of anaphylaxis using preoperative tests available in clinical practice. To develop a personalized risk forecast platform for general anesthesia-related anaphylaxis, as a first step, we aimed to investigate the feasibility of machine-learning-based classification using clinical features of patients for risk prediction of anesthesia-related anaphylaxis. After data pre-processing, the performance of five classification methods: Logistic Regression Analysis, Support Vector Machine, Random Forest, Linear Discriminant Analysis, and Naïve Bayes), which were integrated with four feature selection methods (Recursive Feature Elimination, Chi-Squared Method, Correlation-based Feature Selection, and Information Gain Ratio), was evaluated using two-layer cross-validation. Seventy-four features, which were defined from 225 participants, were applied for model fitting. Linear Discriminant Analysis in conjunction with Recursive Feature Elimination showed good performance, with accuracy of 0.867 and Matthews correlation coefficient (MCC) of 0.558 with 25 features used in the classification. Logistic Regression in conjunction with Recursive Feature Elimination model also showed adequate performance, with accuracy of 0.858 and MCC of 0.541 with six features used in the classification. This study presents initial proof of the capability of a machine-learning-based strategy for forecasting low-prevalence anesthesia-related anaphylaxis from a clinical perspective. It could provide a basis for establishing an effective risk-scoring and predictive system for perioperative anaphylaxis that would help identify preoperatively whether anaphylaxis will occur and could be used to predict unstable patient states preceding anaphylactic shock.