Applied Machine Learning for the Prediction of Growth of Abdominal Aortic Aneurysm in Humans

Figures

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  Figure 1. Receiver operating curve (ROC) demonstrating the ability of the logistic regression model to discern future growth at predefined growth rate thresholds. ROC curves were first plotted using two variables (baseline FMD and AAA diameter) to analyse the performance of the generalised linear logistic regression model. The ROC curves are plotted with the threshold of “stable/no growth“ (A) (defined as growth ≤ 0mm/year) or “fast growth” (B) (defined as upper tertile of growth within the group, during the respective period) at both 12 and 24 months (blue and red line respectively).

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  Figure 2. Applying machine learning techniques for the prediction of AAA growth in individual patients. For the prediction of AAA diameter in individual patients at 12 (A) and 24 (B) months from baseline, non-linear kernel support vector regression (SVM) was applied using two features (FMD, AAA diameter), and hyperparameter optimisation using nested fivefold cross validations. The SVM method is a non-linear regression which can potentially improve the accuracy of predicting AAA diameter by considering non-linear functions of the input features. A 2 mm error margin was allowed because this is accepted technical variability between ultrasound diameter measurements in AAAs. The algorithm predicted the individual's AAA diameter to within a 2 mm error in 85% and 71% of patients at 12 and 24 months, respectively (with root mean square error of 1.7 and 2.4, respectively). Note. The figure includes only data points that are within the 2 mm error tolerance. Black cross, actual AAA diameter measured at 12 and 24 months; blue and red circles, machine predicted diameter at 12 (blue) and 24 (red) months.