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Lookup NU author(s): Professor Djordje JakovljevicORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2021 The Author(s)Virtual population generation is an emerging field in data science with numerous applications in healthcare towards the augmentation of clinical research databases with significant lack of population size. However, the impact of data augmentation on the development of AI (artificial intelligence) models to address clinical unmet needs has not yet been investigated. In this work, we assess whether the aggregation of real with virtual patient data can improve the performance of the existing risk stratification and disease classification models in two rare clinical domains, namely the primary Sjögren's Syndrome (pSS) and the hypertrophic cardiomyopathy (HCM), for the first time in the literature. To do so, multivariate approaches, such as, the multivariate normal distribution (MVND), and straightforward ones, such as, the Bayesian networks, the artificial neural networks (ANNs), and the tree ensembles are compared against their performance towards the generation of high-quality virtual data. Both boosting and bagging algorithms, such as, the Gradient boosting trees (XGBoost), the AdaBoost and the Random Forests (RFs) were trained on the augmented data to evaluate the performance improvement for lymphoma classification and HCM risk stratification. Our results revealed the favorable performance of the tree ensemble generators, in both domains, yielding virtual data with goodness-of-fit 0.021 and KL-divergence 0.029 in pSS and 0.029, 0.027 in HCM, respectively. The application of the XGBoost on the augmented data revealed an increase by 10.9% in accuracy, 10.7% in sensitivity, 11.5% in specificity for lymphoma classification and 16.1% in accuracy, 16.9% in sensitivity, 13.7% in specificity in HCM risk stratification.
Author(s): Pezoulas VC, Grigoriadis GI, Gkois G, Tachos NS, Smole T, Bosnic Z, Piculin M, Olivotto I, Barlocco F, Robnik-Sikonja M, Jakovljevic DG, Goules A, Tzioufas AG, Fotiadis DI
Publication type: Article
Publication status: Published
Journal: Computers in Biology and Medicine
Year: 2021
Volume: 134
Print publication date: 01/07/2021
Online publication date: 06/06/2021
Acceptance date: 24/05/2021
Date deposited: 16/08/2023
ISSN (print): 0010-4825
ISSN (electronic): 1879-0534
Publisher: Elsevier Ltd
URL: https://doi.org/10.1016/j.compbiomed.2021.104520
DOI: 10.1016/j.compbiomed.2021.104520
PubMed id: 34118751
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