Evaluation of Synthetic Data Generation Models for Balancing Multiclass Metabolomic Profiles
Authors: 
Saioa Elizondo, Mikel Hernandez, Francisco J. Londono-Hoyos, Andoni Beristain, Rubén Gil Redondo, Ricardo Conde, Oscar Millet, Gorka Epelde UnanueAuthors Info & Claims
Saioa Elizondo, Mikel Hernandez, Francisco J. Londono-Hoyos, Andoni Beristain, Rubén Gil Redondo, Ricardo Conde, Oscar Millet, Gorka Epelde UnanueAuthors Info & ClaimsPages 56 - 61
Abstract
Balancing highly imbalanced multiclass datasets, a crucial step for effective classification in health and well-being analysis, remains challenging, especially with conditions like Metabolic Syndrome (MetS). This study evaluates Synthetic Data Generation (SDG) techniques for balancing a dataset of metabolomic profiles associated with MetS. Evaluating models like Gaussian Copula, FAST ML, CTGAN, TVAE, TabDDPM, and Tabsyn reveals their effectiveness in preserving multivariate relationships but dissimilar success in replicating individual variable distributions. Results show significant improvements in classification tasks with balanced data, highlighting the potential of SDG in enhancing real-world data analysis and decision-making in health research.
References
[1]
Shuo Wang, & Xin Yao (2012). Multiclass Imbalance Problems: Analysis and Potential Solutions. IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society, 42(4), 1119–1130.
[2]
Fujiwara, K., Huang, Y., Hori, K., Nishioji, K., Kobayashi, M., Kamaguchi, M., & Kano, M. (2020). Over- and Under-sampling Approach for Extremely Imbalanced and Small Minority Data Problem in Health Record Analysis. Frontiers in public health, 8, 178.
[3]
Abbasian M, Ebrahimi H, Delvarianzadeh M, Norouzi P, Fazli M. Association between serum uric acid (SUA) levels and metabolic syndrome (MetS) components in personnel of shahroud university of medical sciences. Diabetes Metab Syndr (2016) 10(3):132–6.
[4]
Alberti, K. G., Eckel, R. H., Grundy, S. M., Zimmet, P. Z., Cleeman, J. I., Donato, K. A., ... & Smith Jr, S. C. (2009). Harmonizing the metabolic syndrome: a joint interim statement of the international diabetes federation task force on epidemiology and prevention; national heart, lung, and blood institute; American heart association; world heart federation; international atherosclerosis society; and international association for the study of obesity. Circulation, 120(16), 1640-1645.
[5]
Bruzzone, C., Gil-Redondo, R., Seco, M., Barragán, R., de la Cruz, L., Cannet, C., ... & Millet, O. (2021). A molecular signature for the metabolic syndrome by urine metabolomics. Cardiovascular Diabetology, 20(1), 155.
[6]
Harsurinder Kaur, Husanbir Singh Pannu, and Avleen Kaur Malhi. 2019. A Systematic Review on Imbalanced Data Challenges in Machine Learning: Applications and Solutions. ACM Comput. Surv. 52, 4, Article 79 (July 2020), 36 pages.
[7]
Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). "SMOTE: Synthetic minority over-sampling technique." Journal of artificial intelligence research, 16, 321-357.
[8]
H. Han, W.-Y. Wang, and B.-H. Mao, ‘Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning’, in Advances in Intelligent Computing, vol. 3644, D.-S. Huang, X.-P. Zhang, and G.-B. Huang, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005, pp. 878–887.
[9]
Hernadez, M., Epelde, G., Alberdi, A., Cilla, R., & Rankin, D. (2023). Synthetic tabular data evaluation in the health domain covering resemblance, utility, and privacy dimensions. Methods of Information in Medicine, 62(S 01), e19-e38.
[10]
Kim, M. (2020, April 30). SMOTE: Practical Consideration & Limitations. Retrieved from https://medium.com/@minjukim023/smote-practical-consideration-limitations-f0d926b661a8
[11]
Kiran, A., & Kumar, S. S. (2023, March). A comparative analysis of gan and vae based synthetic data generators for high dimensional, imbalanced tabular data. In 2023 2nd International Conference for Innovation in Technology (INOCON) (pp. 1-6). IEEE.
[12]
Ahsan, M. M., Ali, M. S., & Siddique, Z. (2024). Enhancing and improving the performance of imbalanced class data using novel GBO and SSG: A comparative analysis. Neural Networks, 173, 106157.
[13]
Pan, T., Pedrycz, W., Yang, J., & Wang, J. (2024). An improved generative adversarial network to oversample imbalanced datasets. Engineering Applications of Artificial Intelligence, 132, 107934.
[14]
N. Patki, R. Wedge and K. Veeramachaneni, "The Synthetic Data Vault," 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA), Montreal, QC, Canada, 2016, pp. 399-410.
[15]
Xu, L., Skoularidou, M., Cuesta-Infante, A., & Veeramachaneni, K. (2019). Modeling Tabular data using Conditional GAN. Neural Information Processing Systems.
[16]
Kotelnikov, A., Baranchuk, D., Rubachev, I., & Babenko, A. (2023, July). Tabddpm: Modelling tabular data with diffusion models. In International Conference on Machine Learning (pp. 17564-17579). PMLR.
[17]
Zhang, H., Zhang, J., Srinivasan, B., Shen, Z., Qin, X., Faloutsos, C., ... & Karypis, G. (2023). Mixed-type tabular data synthesis with score-based diffusion in latent space. arXiv preprint arXiv:2310.09656.
[18]
Vallevik, V. B., Babic, A., Marshall, S. E., Elvatun, S., Brøgger, H., Alagaratnam, S., ... & Nygård, J. F. (2024). Can I trust my fake data–A comprehensive quality assessment framework for synthetic tabular data in healthcare. arXiv preprint arXiv:2401.13716.
[19]
Hernadez, M., Epelde, G., Alberdi, A., Cilla, R., & Rankin, D. (2023). Synthetic tabular data evaluation in the health domain covering resemblance, utility, and privacy dimensions. Methods of Information in Medicine, 62(S 01), e19-e38.
[20]
Murtaza, H., Ahmed, M., Khan, N. F., Murtaza, G., Zafar, S., & Bano, A. (2023). Synthetic data generation: State of the art in health care domain. Computer Science Review, 48, 100546.
[21]
Dankar, F. K., Ibrahim, M. K., & Ismail, L. (2022). A multi-dimensional evaluation of synthetic data generators. IEEE Access, 10, 11147-11158.
[22]
Trygg, J., & Wold, S. (2002). Orthogonal projections to latent structures (O-PLS). Journal of Chemometrics, 16(3), 119-128.
[23]
Eriksson, L., Johansson, E., Kettaneh-Wold, N., Trygg, J., Wikström, C., & Wold, S. (2006). Multi- and Megavariate Data Analysis Part I: Basic Principles and Applications. Umetrics Academy.
Index Terms
- Evaluation of Synthetic Data Generation Models for Balancing Multiclass Metabolomic Profiles
Index terms have been assigned to the content through auto-classification.
Recommendations
Comparative Evaluation of Oversampling Techniques for Balancing Metabolic Profiles
ICBRA '22: Proceedings of the 9th International Conference on Bioinformatics Research and ApplicationsThe problem of imbalanced data is common when applying data analytics paradigms to binary and multiclass data, such as statistical analyses, predictive models, and classification metrics sensitive to imbalanced data, i.e., accuracy. Although there exist ...
Implementation and Evaluation of Models for Synthetic Generation of Spectral Signals to Balance Metabolomic Data
ICBRA '24: Proceedings of the 11th International Conference on Bioinformatics Research and ApplicationsAlthough the advance of Artificial Intelligence (AI) in medicine is significant, its potential impact depends on access to quality data. A particular challenge is class imbalance in medical data sets. This imbalance can introduce biases into AI models ...
Synthetic Generation of High-Dimensional Datasets
Generation of synthetic datasets is a common practice in many research areas. Such data is often generated to meet specific needs or certain conditions that may not be easily found in the original, real data. The nature of the data varies according to ...
Comments
Information & Contributors
Information
Published In
September 2024
144 pages
ISBN:9798400717536
DOI:10.1145/3700666
Copyright © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 13 January 2025
Check for updates
Author Tags
Qualifiers
- Research-article
Funding Sources
- Industry, Energy Transition and Sustainability department of the Basque country: ELKARTEK 2024 call
Conference
ICBRA '24
ICBRA '24: Proceedings of the 11th International Conference on Bioinformatics Research and Applications
September 13 - 15, 2024
Milan, Italy
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 2Total Downloads
- Downloads (Last 12 months)2
- Downloads (Last 6 weeks)2
Reflects downloads up to 17 Jan 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in