Refereed journal article or data article (A1)

Synthetic minority oversampling of vital statistics data with generative adversarial networks




List of Authors: Aki Koivu, Mikko Sairanen, Antti Airola, Tapio Pahikkala

Publisher: Oxford University Press

Publication year: 2020

Journal: Journal of the American Medical Informatics Association

Journal acronym: JAMIA

eISSN: 1527-974X

DOI: http://dx.doi.org/10.1093/jamia/ocaa127

URL: https://doi.org/10.1093/jamia/ocaa127

Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/48930675


Abstract
Objective

Minority oversampling is a standard approach used for adjusting the ratio between the classes on imbalanced data. However, established methods often provide modest improvements in classification performance when applied to data with extremely imbalanced class distribution and to mixed-type data. This is usual for vital statistics data, in which the outcome incidence dictates the amount of positive observations. In this article, we developed a novel neural network-based oversampling method called actGAN (activation-specific generative adversarial network) that can derive useful synthetic observations in terms of increasing prediction performance in this context.

Materials and Methods

From vital statistics data, the outcome of early stillbirth was chosen to be predicted based on demographics, pregnancy history, and infections. The data contained 363 560 live births and 139 early stillbirths, resulting in class imbalance of 99.96% and 0.04%. The hyperparameters of actGAN and a baseline method SMOTE-NC (Synthetic Minority Over-sampling Technique-Nominal Continuous) were tuned with Bayesian optimization, and both were compared against a cost-sensitive learning-only approach.

Results

While SMOTE-NC provided mixed results, actGAN was able to improve true positive rate at a clinically significant false positive rate and area under the curve from the receiver-operating characteristic curve consistently.

Discussion

Including an activation-specific output layer to a generator network of actGAN enables the addition of information about the underlying data structure, which overperforms the nominal mechanism of SMOTE-NC.

Conclusions

actGAN provides an improvement to the prediction performance for our learning task. Our developed method could be applied to other mixed-type data prediction tasks that are known to be afflicted by class imbalance and limited data availability.


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Last updated on 2022-13-06 at 16:17