{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T06:09:17Z","timestamp":1779170957703,"version":"3.51.4"},"reference-count":44,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T00:00:00Z","timestamp":1779148800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T00:00:00Z","timestamp":1779148800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Netw Model Anal Health Inform Bioinforma"],"DOI":"10.1007\/s13721-026-00747-x","type":"journal-article","created":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T05:47:56Z","timestamp":1779169676000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A novel TEA-CGAN framework for mitigating class imbalance in disease risk prediction"],"prefix":"10.1007","volume":"15","author":[{"given":"Ying","family":"Pan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bai","family":"Peng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mengying","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-0710-8040","authenticated-orcid":false,"given":"Li","family":"Ma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Limei","family":"Peng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,5,19]]},"reference":[{"key":"747_CR1","doi-asserted-by":"publisher","first-page":"375","DOI":"10.1007\/978-3-031-41352-0_19","volume-title":"Sustainable statistical and data science methods and practices: Reports from lisa 2020 global network, ghana, 2022","author":"O Awe","year":"2023","unstructured":"Awe O, Ojumu JB, Ayanwoye GA, jumoola JS, Dias R (2023) Machine learning approaches for handling imbalances in health data classification. Sustainable statistical and data science methods and practices: Reports from lisa 2020 global network, ghana, 2022. Springer Nature Switzerland, pp 375\u2013391. https:\/\/doi.org\/10.1007\/978-3-031-41352-0_19"},{"key":"747_CR2","doi-asserted-by":"publisher","DOI":"10.21037\/jtd.2019.01.25","volume":"36","author":"JM Bae","year":"2014","unstructured":"Bae JM (2014) The clinical decision analysis using decision tree. Epidemiol Health 36:e2014025. https:\/\/doi.org\/10.21037\/jtd.2019.01.25","journal-title":"Epidemiol Health"},{"key":"747_CR3","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-24958-7_85","author":"S Barua","year":"2011","unstructured":"Barua S, Islam MM, Murase K (2011) A novel synthetic minority oversampling technique for imbalanced data set learning. Neural information processing. https:\/\/doi.org\/10.1007\/978-3-642-24958-7_85","journal-title":"Neural information processing"},{"key":"747_CR4","doi-asserted-by":"publisher","DOI":"10.1145\/3544558","volume":"55","author":"M Bayer","year":"2022","unstructured":"Bayer M, Kaufhold M-A, Reuter C (2022) A survey on data augmentation for text classification. ACM Comput Surv 55:146.1-146.39. https:\/\/doi.org\/10.1145\/3544558","journal-title":"ACM Comput Surv"},{"key":"747_CR5","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-023-10662-6","volume":"57","author":"K Berahmand","year":"2024","unstructured":"Berahmand K, Daneshfar F, Salehi ES, Li Y, Xu Y (2024) Autoencoders and their applications in machine learning: a survey. Artif Intell Rev 57:28. https:\/\/doi.org\/10.1007\/s10462-023-10662-6","journal-title":"Artif Intell Rev"},{"key":"747_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1471-2105-14-106","volume":"14","author":"R Blagus","year":"2013","unstructured":"Blagus R, Lusa L (2013) Smote for high-dimensional class-imbalanced data. BMC Bioinf 14:1\u201316. https:\/\/doi.org\/10.1186\/1471-2105-14-106","journal-title":"BMC Bioinf"},{"key":"747_CR7","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) Smote: synthetic minority over-sampling technique. J Artif Intell Res 16:321\u2013357. https:\/\/doi.org\/10.1613\/jair.953","journal-title":"J Artif Intell Res"},{"key":"747_CR8","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-024-01076-x","volume":"7","author":"JN Eckardt","year":"2024","unstructured":"Eckardt JN, Hahn W, Rllig C, Stasik S, Platzbecker U, M\u00fcller-Tidow C, Schfer-Eckart K (2024) Mimicking clinical trials with synthetic acute myeloid leukemia patients using generative artificial intelligence. NPJ Digit Med 7:76. https:\/\/doi.org\/10.1038\/s41746-024-01076-x","journal-title":"NPJ Digit Med"},{"key":"747_CR9","doi-asserted-by":"publisher","DOI":"10.1007\/11538059_91","author":"H Han","year":"2005","unstructured":"Han H, Wang W-Y, Mao B-H (2005) Borderline-smote: a new over-sampling method in imbalanced data sets learning. Advances in intelligent computing. https:\/\/doi.org\/10.1007\/11538059_91","journal-title":"Advances in intelligent computing"},{"key":"747_CR10","doi-asserted-by":"publisher","unstructured":"Hasanin T, Khoshgoftaar T (2018) The effects of random undersampling with simulated class imbalance for big data. 2018 ieee international conference on information reuse and integration (iri) (p. 70-79). https:\/\/doi.org\/10.1109\/IRI.2018.00018","DOI":"10.1109\/IRI.2018.00018"},{"key":"747_CR11","doi-asserted-by":"publisher","unstructured":"He H, Bai Y, Garcia EA, Li S (2008) Adasyn : Adaptive synthetic sampling approach for imbalanced learning. 2008 ieee international joint conference on neural networks (ieee world congress on computational intelligence) (p. 1322\u20131328). https:\/\/doi.org\/10.1109\/IJCNN.2008.4633969","DOI":"10.1109\/IJCNN.2008.4633969"},{"key":"747_CR12","doi-asserted-by":"publisher","first-page":"547","DOI":"10.1016\/j.eij.2022.05.006","volume":"3","author":"AS Imran","year":"2022","unstructured":"Imran AS, Yang R, Kastrati Z, Daudpota SM, Shaikh S (2022) The impact of synthetic text generation for sentiment analysis using gan based models. Egypt Inform J 3:547\u2013557. https:\/\/doi.org\/10.1016\/j.eij.2022.05.006","journal-title":"Egypt Inform J"},{"key":"747_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2025.112840","author":"RWMHXZY Jia","year":"2025","unstructured":"Jia RWMHXZY (2025) Dptstrip: adversarially robust learning with distance-aware point-to-set triplet loss. Pattern Recogn. https:\/\/doi.org\/10.1016\/j.patcog.2025.112840","journal-title":"Pattern Recogn"},{"key":"747_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.122778","volume":"244","author":"AA Khan","year":"2024","unstructured":"Khan AA, Chaudhari O, Chandra R (2024) A review of ensemble learning and data augmentation models for class imbalanced problems: combination, implementation and evaluation. Expert Syst Appl 244:122778. https:\/\/doi.org\/10.1016\/j.eswa.2023.122778","journal-title":"Expert Syst Appl"},{"key":"747_CR15","unstructured":"Lei X, Alfredo C-I, Maria S, Kalyan V (2019) Modeling tabular data using conditional gan. Adv Neural Inf Process Syst (p. 11)"},{"key":"747_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2023.110176","author":"PLYPJ Li","year":"2023","unstructured":"Li PLYPJ (2023) A comprehensive survey on design and application of autoencoder in deep learning. Appl Soft Comput. https:\/\/doi.org\/10.1016\/j.asoc.2023.110176","journal-title":"Appl Soft Comput"},{"key":"747_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcp.2022.111801","volume":"474","author":"Y Liu","year":"2023","unstructured":"Liu Y, Ponce C, Brunton SL, Kutz JN (2023) Multiresolution convolutional autoencoders. Pattern Recogn 474:111801. https:\/\/doi.org\/10.1016\/j.jcp.2022.111801","journal-title":"Pattern Recogn"},{"key":"747_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2024.120117","volume":"661","author":"Y Liu","year":"2024","unstructured":"Liu Y, Zhu L, Ding L, Sui H, Shang W (2024) A hybrid sampling method for highly imbalanced and overlapped data classification with complex distribution. Inf Sci 661:120117. https:\/\/doi.org\/10.1016\/j.ins.2024.120117","journal-title":"Inf Sci"},{"key":"747_CR19","doi-asserted-by":"publisher","unstructured":"Lopez Pinaya WH, Vieira S, Garcia-Dias R, Mechelli A (2020) Chapter 11 - autoencoders. In: Mechelli A, Vieira S (eds) Machine learning. Academic Press, pp 193\u2013208. https:\/\/doi.org\/10.1016\/B978-0-12-815739-8.00011-0","DOI":"10.1016\/B978-0-12-815739-8.00011-0"},{"key":"747_CR20","doi-asserted-by":"publisher","first-page":"4980","DOI":"10.1109\/TIP.2020.2977573","volume":"29","author":"J Ma","year":"2020","unstructured":"Ma J, Xu H, Jiang J, Mei X, Zhang X-P (2020) Ddcgan: a dual-discriminator conditional generative adversarial network for multi-resolution image fusion. ITIP 29:4980\u20134995. https:\/\/doi.org\/10.1109\/TIP.2020.2977573","journal-title":"ITIP"},{"key":"747_CR21","doi-asserted-by":"publisher","first-page":"445","DOI":"10.1007\/s11063-015-9430-9","volume":"43","author":"J Maria","year":"2016","unstructured":"Maria J, Amaro J, Falcao G, Alexandre LA (2016) Stacked autoencoders using low-power accelerated architectures for object recognition in autonomous systems. Neural Process Lett 43:445\u2013458. https:\/\/doi.org\/10.1007\/s11063-015-9430-9","journal-title":"Neural Process Lett"},{"key":"747_CR22","doi-asserted-by":"publisher","unstructured":"Mohammed R, Rawashdeh J, Abdullah M (2020). Machine learning with oversampling and undersampling techniques Overview study and experimental results. 2020 11th international conference on information and communication systems (icics) (p. 243\u2013248). https:\/\/doi.org\/10.1109\/ICICS49469.2020.239556","DOI":"10.1109\/ICICS49469.2020.239556"},{"key":"747_CR23","doi-asserted-by":"publisher","unstructured":"Moreo A, Esuli A, Sebastiani F (2016) Distributional random oversampling for imbalanced text classification. Proceedings of the 39th international acm sigir conference on research and development in information retrieval (p. 805\u2013808). https:\/\/doi.org\/10.1145\/2911451.2914722","DOI":"10.1145\/2911451.2914722"},{"key":"747_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2016.09.018","author":"WZYYY Peng","year":"2016","unstructured":"Peng WZYYY (2016) Dictionary learning based on discriminative energy contribution for image classification. Knowl Based Syst. https:\/\/doi.org\/10.1016\/j.knosys.2016.09.018","journal-title":"Knowl Based Syst"},{"key":"747_CR25","doi-asserted-by":"publisher","unstructured":"Rezaei M, Uemura T, N\u00e4ppi J, Yoshida H, Lippert C, Meinel C (2020) Generative synthetic adversarial network for internal bias correction and handling class imbalance problem in medical image diagnosis. Medical imaging 2020: Computer-aided diagnosis (p. 113140E). https:\/\/doi.org\/10.1117\/12.2551166","DOI":"10.1117\/12.2551166"},{"key":"747_CR26","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-022-00648-6","volume":"9","author":"S-C Rick","year":"2022","unstructured":"Rick S-C, M KT (2022) The use of generative adversarial networks to alleviate class imbalance in tabular data: a survey. J Big Data 9:98. https:\/\/doi.org\/10.1186\/s40537-022-00648-6","journal-title":"J Big Data"},{"key":"747_CR27","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-022-10150-3","author":"MS Santos","year":"2022","unstructured":"Santos MS, Abreu PH, Japkowicz N, Fern\u00e1ndez A, Soares C, Wilk S, Santos J (2022) On the joint-effect of class imbalance and overlap: a critical review. Artif Intell Rev. https:\/\/doi.org\/10.1007\/s10462-022-10150-3","journal-title":"Artif Intell Rev"},{"key":"747_CR28","doi-asserted-by":"publisher","unstructured":"Schroff F, Kalenichenko D, Philbin J (2015) Facenet: A unified embedding for face recognition and clustering. 2015 ieee conference on computer vision and pattern recognition (cvpr) (p. 815\u2013823). https:\/\/doi.org\/10.1109\/CVPR.2015.7298682","DOI":"10.1109\/CVPR.2015.7298682"},{"key":"747_CR29","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1016\/j.neucom.2021.10.093","volume":"471","author":"D Shi","year":"2022","unstructured":"Shi D, Zhao C, Wang Y, Yang H, Wang G, Jiang H, Zhang Y (2022) Multi actor hierarchical attention critic with rnn-based feature extraction. Neurocomputing 471:79\u201393. https:\/\/doi.org\/10.1016\/j.neucom.2021.10.093","journal-title":"Neurocomputing"},{"issue":"4","key":"747_CR30","doi-asserted-by":"publisher","first-page":"S574","DOI":"10.21037\/jtd.2019.01.25","volume":"11","author":"ME Shipe","year":"2019","unstructured":"Shipe ME, Deppen SA, Farjah F, Grogan EL (2019) Developing prediction models for clinical use using logistic regression: an overview. J Thorac Dis 11(4):S574\u2013S584. https:\/\/doi.org\/10.21037\/jtd.2019.01.25","journal-title":"J Thorac Dis"},{"key":"747_CR31","doi-asserted-by":"publisher","first-page":"55","DOI":"10.3390\/e24010055","volume":"24","author":"A Singh","year":"2022","unstructured":"Singh A, Ogunfunmi T (2022) An overview of variational autoencoders for source separation, finance, and bio-signal applications. Entropy 24:55. https:\/\/doi.org\/10.3390\/e24010055","journal-title":"Entropy"},{"key":"747_CR32","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-024-01421-0","volume":"8","author":"C Sun","year":"2025","unstructured":"Sun C, Dumontier M (2025) Generating unseen diseases patient data using ontology enhanced generative adversarial networks. NPJ Digit Med 8:4. https:\/\/doi.org\/10.1038\/s41746-024-01421-0","journal-title":"NPJ Digit Med"},{"key":"747_CR33","doi-asserted-by":"publisher","first-page":"674","DOI":"10.1016\/j.patcog.2018.03.008","volume":"81","author":"D Swagatam","year":"2018","unstructured":"Swagatam D, Shounak D, Bidyut BC (2018) Handling data irregularities in classification: foundations, trends, and future challenges. Patt Recog 81:674\u2013693. https:\/\/doi.org\/10.1016\/j.patcog.2018.03.008","journal-title":"Patt Recog"},{"key":"747_CR34","doi-asserted-by":"publisher","first-page":"10096","DOI":"10.1109\/ACCESS.2016.2611583","volume":"4","author":"S Tariyal","year":"2016","unstructured":"Tariyal S, Majumdar A, Singh R, Vatsa M (2016) Deep dictionary learning. IEEE Access 4:10096\u201310109. https:\/\/doi.org\/10.1109\/ACCESS.2016.2611583","journal-title":"IEEE Access"},{"key":"747_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2020.03.336","author":"NP Tigga","year":"2020","unstructured":"Tigga NP, Garg S (2020) Prediction of type 2 diabetes using machine learning classification methods. Procedia Comput Sci. https:\/\/doi.org\/10.1016\/j.procs.2020.03.336","journal-title":"Procedia Comput Sci"},{"key":"747_CR36","doi-asserted-by":"publisher","first-page":"1","DOI":"10.32913\/mic-ict-research.v2020.n1.894","volume":"2020","author":"L Vu","year":"2020","unstructured":"Vu L, Nguyen QU (2020) Handling imbalanced data in intrusion detection systems using generative adversarial networks. J Res Dev Inf Commun Technol 2020:1\u201313. https:\/\/doi.org\/10.32913\/mic-ict-research.v2020.n1.894","journal-title":"J Res Dev Inf Commun Technol"},{"key":"747_CR37","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.1972.4309137","author":"DL Wilson","year":"1972","unstructured":"Wilson DL (1972) Asymptotic properties of nearest neighbor rules using edited data. IEEE Trans Syst Man Cybern SMC-2. https:\/\/doi.org\/10.1109\/TSMC.1972.4309137","journal-title":"IEEE Trans Syst Man Cybern SMC-2"},{"key":"747_CR38","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2023.119234","volume":"643","author":"L Xu","year":"2023","unstructured":"Xu L, Xu L, Yu J (2023) Time series imputation with gan inversion and decay connection. Inf Sci 643:119234. https:\/\/doi.org\/10.1016\/j.ins.2023.119234","journal-title":"Inf Sci"},{"key":"747_CR39","doi-asserted-by":"publisher","unstructured":"Xu Z, Qi C, Xu G (2019) Semi-supervised attention-guided cyclegan for data augmentation on medical images. 2019 ieee international conference on bioinformatics and biomedicine (bibm) (p. 563\u2013568). https:\/\/doi.org\/10.1109\/BIBM47256.2019.8982932","DOI":"10.1109\/BIBM47256.2019.8982932"},{"key":"747_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2023.110745","volume":"276","author":"H Yan","year":"2023","unstructured":"Yan H, Cui Z, Luo X, Wang R, Yao Y (2023) Emphasizing feature inter-class separability for improving highly imbalanced overlapped data classification. Knowl Based Syst 276:110745. https:\/\/doi.org\/10.1016\/j.knosys.2023.110745","journal-title":"Knowl Based Syst"},{"key":"747_CR41","doi-asserted-by":"publisher","first-page":"5718","DOI":"10.1016\/j.eswa.2008.06.108","volume":"36","author":"S-J Yen","year":"2009","unstructured":"Yen S-J, Lee Y-S (2009) Cluster-based under-sampling approaches for imbalanced data distributions. Expert Syst Appl 36:5718\u20135727. https:\/\/doi.org\/10.1016\/j.eswa.2008.06.108","journal-title":"Expert Syst Appl"},{"key":"747_CR42","doi-asserted-by":"publisher","first-page":"483","DOI":"10.1016\/j.physa.2006.07.023","volume":"374","author":"S Zhang","year":"2007","unstructured":"Zhang S, Wang R, Zhang X (2007) Identification of overlapping community structure in complex networks using fuzzy c-means clustering. Physica A 374:483\u2013490","journal-title":"Physica A"},{"key":"747_CR43","doi-asserted-by":"publisher","first-page":"1397","DOI":"10.1016\/j.ins.2022.07.145","volume":"609","author":"B Zhu","year":"2022","unstructured":"Zhu B, Pan X, vanden Broucke S, Xiao J (2022) A gan-based hybrid sampling method for imbalanced customer classification. Inf Sci 609:1397\u20131411. https:\/\/doi.org\/10.1016\/j.ins.2022.07.145","journal-title":"Inf Sci"},{"key":"747_CR44","doi-asserted-by":"publisher","first-page":"349","DOI":"10.1007\/978-3-319-93040-4_28","volume":"10939","author":"X Zhu","year":"2018","unstructured":"Zhu X, Liu Y, Li J, Wan T, Qin Z (2018) Emotion classification with data augmentation using generative adversarial networks. Lect Notes Comput Sci 10939:349\u2013360. https:\/\/doi.org\/10.1007\/978-3-319-93040-4_28","journal-title":"Lect Notes Comput Sci"}],"container-title":["Network Modeling Analysis in Health Informatics and Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13721-026-00747-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13721-026-00747-x","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13721-026-00747-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T05:47:58Z","timestamp":1779169678000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13721-026-00747-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,19]]},"references-count":44,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["747"],"URL":"https:\/\/doi.org\/10.1007\/s13721-026-00747-x","relation":{},"ISSN":["2192-6670"],"issn-type":[{"value":"2192-6670","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,19]]},"assertion":[{"value":"4 October 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 January 2026","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 January 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 May 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The writers do not have any relevant conflicts of interest to disclose about the subject matter of this work.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable. This study used datasets from the public Kaggle and UCI Machine Learning Repository. The patients involved in these datasets have obtained ethical approval. Users can download these datasets for free for research and publish articles. Thus, there are no ethical issues or other conflicts of interest.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}],"article-number":"120"}}