Assessing K-Nearest Neighbours Algorithm for Simple, Interpretable Time-to-Event Survival Predictions Over a Range of Simulated Datasets

Survival prediction is a key task in medicine. Existing models are based on statistical techniques, such as the Cox models and there is limited work on the application of machine learning. In this paper we demonstrate that the K-Nearest Neighbour algorithm can be used for survival prediction. We show that its performance is as good as that of standard techniques, and that it provides a clear interpretation of the results. We show that pre-processing methods improve performance, and evaluate the performance across 20 different datasets with differing properties to show that the model performs well under various conditions. For low event rate datasets we show that KNN can outperform the Cox model.