Addressing the class imbalance problem in medical datasets
Open accessAbstract
A well balanced dataset is very important for creating a good prediction model.Medical datasets are often not balanced in their class labels.Most existing classification methods tend to perform poorly on minority class examples when the dataset is extremely imbalanced.This is because they aim to optimize the overall accuracy without considering the relative distribution of each class.In this paper we examine the performance of over-sampling and under-sampling techniques to balance cardiovascular data.Well known over-sampling technique SMOTE is used and some under-sampling techniques are also explored.An improved under sampling technique is proposed.Experimental results show that the proposed method displays significant better performance than the existing methods.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Strategies involving under-sampling controls, pseudo-sampling cases, and combinations have been developed to address class imbalance and shown to improve model performance.
“A number of strategies involving under-sampling controls, pseudo-sampling cases, and combinations have been developed to address this problem (83) and shown to improve model performance [e.g., (84, 85)].”
From Using Administrative Data to Predict Suicide After Psychiatric Hospitalization in the Veterans Health Administration System (2020) · cites this paper as [85]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
SMOTE synthetic minority over-sampling technique
Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002) · Journal of Artificial Intelligence Research · doi:10.1613/jair.953 · Cited by 32,865 · see citing works
Data mining: concepts and techniques
Sharma, R., Shah, K., Shirahatti, Y., Patel, S., Sharma, R., Shah, K., Patel, S., Sharma, R., & Han, J. (2012) · Choice Reviews Online · doi:10.5860/choice.49-3305 · Cited by 28,655 · see citing works
C4.5, Class Imbalance, and Cost Sensitivity: Why Under-Sampling beats Over-Sampling
Drummond, C., & Holte, R. C. (2003) · Cited by 829 · see citing works
KNN approach to unbalanced data distributions: a case study involving information extraction
Ziang, J. (2003) · International Conference on Machine Learning · Cited by 793 · see citing works
Cluster-based under-sampling approaches for imbalanced data distributions
Yen, S.-J., & Lee, Y. (2008) · Expert Systems with Applications · doi:10.1016/j.eswa.2008.06.108 · Cited by 746 · see citing works
A Survey of Evolutionary Algorithms for Decision-Tree Induction
Barros, R. C., Basgalupp, M. P., de Carvalho, A. C. P. L. F., & Freitas, A. A. (2011) · IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) · doi:10.1109/tsmcc.2011.2157494 · Cited by 347 · see citing works
Combining integrated sampling with SVM ensembles for learning from imbalanced datasets
Liu, Y., Yu, X., Huang, J. X., et al. (2010) · Information Processing & Management · doi:10.1016/j.ipm.2010.11.007 · Cited by 164 · see citing works
Efficient Multiclass ROC Approximation by Decomposition via Confusion Matrix Perturbation Analysis
Landgrebe, T. C. W., & Duin, R. (2008) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.70740 · Cited by 120 · see citing works
Cluster-based majority under-sampling approaches for class imbalance learning
Zhang, Y., Zhang, L., & Wang, Y. (2010) · IEEE International Conference on Information and Financial Engineering · doi:10.1109/icife.2010.5609385 · Cited by 72 · see citing works
Evaluating the effect of unbalanced data in biomedical document classification
Laza, R., Pavón, R., Reboiro‐Jato, M., et al. (2011) · Berichte aus der medizinischen Informatik und Bioinformatik/Journal of integrative bioinformatics · doi:10.1515/jib-2011-177 · Cited by 40 · see citing works
Evaluating the effect of unbalanced data in biomedical document classification
Laza, R., Pavón, R., Reboiro‐Jato, M., et al. (2011) · PubMed · doi:10.2390/biecoll-jib-2011-177 · Cited by 24 · see citing works
Rahman, M. M., & Davis, D. N. (2012) · Cited by 20 · see citing works
Self-citationAn Effective Over-sampling Method for Imbalanced Data Sets Classification
Zhai, Z., Yun, Y., Ma, et al. (2011) · Acta Scientiarum Naturalium Universitatis Sunyatseni · Cited by 16 · see citing works
Lotte, F., Lécuyer, A., & Arnaldi, B. (2007) · International IEEE/EMBS Conference on Neural Engineering · doi:10.1109/cne.2007.369640 · Cited by 16 · see citing works
An Effective Under-Sampling Method for Class Imbalance Data Problem
Kim, M.-S. (2007) · 한국지능시스템학회 국제학술대회 발표논문집 · Cited by 15 · see citing works
An effective over-sampling method for imbalanced data sets classification
Zhai, Y., Ma, N., Ruan, D., et al. (2011) · Ghent University Academic Bibliography (Ghent University) · Cited by 5 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Kessler, R. C., Hwang, I., Hoffmire, C. A., McCarthy, J. F., Petukhova, M. V., Rosellini, A. J., Sampson, N. A., Schneider, A. L., Bradley, P. A., Katz, I. R., et al. (2017) · International Journal of Methods in Psychiatric Research · doi:10.1002/mpr.1575 · Cited by 209 · see citing works
Vignati, A., Mazzetti, S., Giannini, V, Russo, F., Bollito, E., Porpiglia, F., Stasi, M., & Regge, D. (2015) · Physics in Medicine & Biology · doi:10.1088/0031-9155/60/7/2685 · Cited by 122 · see citing works
A multiple combined method for rebalancing medical data with class imbalances
Wang, Y.-C., & Cheng, C.-H. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104527 · Cited by 66 · see citing works
Kessler, R. C., Bauer, M. S., Bishop, T. M., Demler, O. V., Dobscha, S. K., Gildea, S. M., Goulet, J. L., Karras, E., Kreyenbuhl, J., Landes, S. J., et al. (2020) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2020.00390 · Cited by 52 · see citing works
Generating Healthcare Time Series Data for Improving Diagnostic Accuracy of Deep Neural Networks
Maweu, B. M., Shamsuddin, R., Dakshit, S., & Prabhakaran, B. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3077049 · Cited by 35 · see citing works
Cross-Device Federated Learning for Mobile Health Diagnostics: A First Study on COVID-19 Detection
Xia, T., Han, J., Ghosh, A., & Mascolo, C. (2023) · ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp49357.2023.10096427 · Cited by 14 · see citing works
Wu, Y., Chen, J., Mao, K., & Zhang, Y. (2023) · 2023 IEEE Biomedical Circuits and Systems Conference (BioCAS) · doi:10.1109/biocas58349.2023.10388714 · Cited by 13 · see citing works
- Authors
- Rahman, M. M., & Davis, D. N.
- ORCID
- Davis, D. N.
- Venue
- International Journal of Machine Learning and Computing
- Publisher
- EJournal Publishing
- Published
- 2013
- Pages
- 224-228
- ISSN
- 2010-3700 (ISSN-L)
- OpenAlex
- W2334028018
- Cited by
- 387
- References
- 16
- Citations by year
- 2026: 28 · 2025: 40 · 2024: 38 · 2023: 46 · 2022: 49 · 2021: 45 · 2020: 49 · 2019: 27 · 2018: 20 · 2017: 23 · 2016: 8 · 2015: 12 · 2014: 1 · 2013: 1
- FWCI
- 8.11
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- International Journal of Machine Learning and Computing (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Imbalanced Data Classification Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- class imbalance, medical datasets, cardiovascular disease prediction, SMOTE, oversampling, undersampling, minority class, imbalanced classification
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
