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The use of International Classification of Diseases (ICD) codes in healthcarepresents a challenge in selecting relevant codes as features for machinelearning models due to this system's large number of codes. In this study, wecompared several unsupervised feature selection methods for an ICD codedatabase of 49,075 coronary artery disease patients in Alberta, Canada.Specifically, we employed Laplacian Score, Unsupervised Feature Selection forMulti-Cluster Data, Autoencoder Inspired Unsupervised Feature Selection,Principal Feature Analysis, and Concrete Autoencoders with and without ICD treeweight adjustment to select the 100 best features from over 9,000 codes. Weassessed the selected features based on their ability to reconstruct theinitial feature space and predict 90-day mortality following discharge. Ourfindings revealed that the Concrete Autoencoder methods outperformed all othermethods in both tasks. Furthermore, the weight adjustment in the ConcreteAutoencoder method decreased the complexity of features.

Unsupervised Feature Selection to Identify Important ICD-10 Codes for Machine Learning: A Case Study on a Coronary Artery Disease Patient Cohort