Fig 1. An illustrative schematic for AutoPrognosis.

In this depiction, AutoPrognosis constructs an ensemble of three ML pipelines. Pipeline 1 uses the MissForest algorithm to impute missing data, and then compresses the data into a lower-dimensional space using the principal component analysis (PCA) algorithm, before using the random forest algorithm to issue predictions. Pipelines 2 and 3 use different algorithms for imputation, feature processing, classification and calibration. AutoPrognosis uses the algorithm in [19] to make decisions on what pipelines to select and how to tune the pipelines’ parameters.