Non-linear missing data imputation for healthcare data via index-aware autoencoders

Sadaf Kabir () and Leily Farrokhvar ()
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Sadaf Kabir: West Virginia University
Leily Farrokhvar: California State University Northridge

Health Care Management Science, 2022, vol. 25, issue 3, No 8, 484-497

Abstract: Abstract The availability of data in the healthcare domain provides great opportunities for the discovery of new or hidden patterns in medical data, which can eventually lead to improved clinical decision making. Predictive models play a crucial role in extracting this unknown information from data. However, medical data often contain missing values that can degrade the performance of predictive models. Autoencoder models have been widely used as non-linear functions for the imputation of missing data in fields such as computer vision, transportation, and finance. In this study, we assess the shortcomings of autoencoder models for data imputation and propose modified models to improve imputation performance. To evaluate, we compare the performance of the proposed model with five well-known imputation techniques on six medical datasets and five classification methods. Through extensive experiments, we demonstrate that the proposed non-linear imputation model outperforms the other models for all degrees of missing ratios and leads to the highest disease classification accuracy for all datasets.

Keywords: Missing data imputation; Non-linear feature imputation; Machine learning; Index-aware autoencoders; Healthcare data (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s10729-022-09597-1

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