DNAttend - ML framework for predicting patient non-attendance

This model is not currently suitable for predicting patient non-attendance in a real-world healthcare environment.

Note: All example data used in this repository is simulated and for illustrative purposes only.

See code README for installation and usage instructions.

Overview

A CatBoost Classifier for predicting patient non-attendance (DNA).

DNAttend trains two models independently; a baseline logistic regression model and a CatBoost model. The CatBoost model is trained via a cross-validated randomised hyper-parameter search with over-fit detection. In addition, over-fit detection is performed using a holdout validation set to determine the optimal boosting iterations. Output probability of both models are calibrated via cross-validation.

Finally, decision thresholds are tuned, using the training dataset, to optimise either the ROC or F1 score. This choice of threshold metric is determined by the tuneThresholdBy option of the configuration file (defult = f1).

Figure: Overview of DNAttend workflow