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A computational pipeline for data augmentation towards the improvement of disease classification and risk stratification models: A case study in two clinical domains

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.

Publication metadata

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


DOI: 10.1016/j.compbiomed.2021.104520

PubMed id: 34118751


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Funder referenceFunder name
777204Commission of the European Communities