Autoencoder-Based Attribute Noise Handling Method for Medical Data

Abstract

Medical datasets are particularly subject to attribute noise, that is, missing and erroneous values. Attribute noise is known to be largely detrimental to learning performances. To maximize future learning performances, it is primordial to deal with attribute noise before performing any inference. We propose a simple autoencoder-based preprocessing method that can correct mixed-type tabular data corrupted by attribute noise. No other method currently exists to entirely handle attribute noise in tabular data. We experimentally demonstrate that our method outperforms both state-of-the-art imputation methods and noise correction methods on several real-world medical datasets.