Sleep Disordered Breathing

Analysis of nocturnal paediatric oximetry uses trained Support Vector Machines (SVMs). If Regression is selected, a Support Vector Regressor (SVR) is used to predict the AHI. If Classification is selected, a Support Vector Classifier (SVC) assigns a risk classification.

Regression

Predicted AHI

If Regression is selected, an SVR produces a point estimate of the apnoea-hypopnea index (AHI). Measures of uncertainty are provided through the reporting of confidence intervals calculated using a quantile regression model fit using the residuals in testing of holdout Queensland Children's Hospital (QCH) and external PATS polysomnography data.

Classification

Score

If Classification is selected, an SVC calculates the unitless distance of the final prediction to the decision boundary (scale: -inf to +inf, with 0 representing the boundary). The sign is matched to the final prediction: negative values represent a negative result, and positive values a positive result. The magnitude of the Score represents the degree of belonging (to the assigned class) but is not a probability.

Calibrated Probability

The reporting of calibrated probabilities allows clinicians 1) to relate the Score to the probability of the real-world outcome, and 2) to understand the uncertainty surrounding the prediction using confidence intervals. The calibrated probability of an AHI of ≥5 is calculated by comparing the probability of the real occurrences of the event against the Score provided by the SVC, through testing of the SVC on real world data; a combination of holdout QCH and external PATS polysomnography data.

Model Explanations

Feature importance values or explanations attempt to describe the impact of the feature on the classification decision.

Global Explanations

To estimate the overall importance of each feature to the prediction, Global Explanations are approximated using Monte Carlo resampling of support vectors. Global Explanations are the mean of the marginal contributions of each feature. The size of the absolute value is a measure of the overall importance of that feature, and the sign of the value indicates the directionality of the influence on the prediction.

Local Explanations

To understand the behaviour of the SVM near to the classification, a linear model is trained. Local Explanation values are the beta-coefficients of the trained linear model, and provide an estimate of how the prediction changes with small, localised fluctuations in the feature values.