The DOI matches its Crossref record; 46 of the 49 references it cites were found in a registry or an index.
A hybrid machine learning approach to cerebral stroke prediction based on imbalanced medical dataset
Abstract
Background and objective Cerebral stroke has become a significant global public health issue in recent years. The ideal solution to this concern is to prevent in advance by controlling related metabolic factors. However, it is difficult for medical staff to decide whether special precautions are needed for a potential patient only based on the monitoring of physiological indicators unless they are obviously abnormal. This paper will develop a hybrid machine learning approach to predict cerebral stroke for clinical diagnosis based on the physiological data with incompleteness and class imbalance.Methods Two steps are involved in the whole process. Firstly, random forest regression is adopted to impute missing values before classification. Secondly, an automated hyperparameter optimization(AutoHPO) based on deep neural network(DNN) is applied to stroke prediction on an imbalanced dataset.Results The medical dataset contains 43,400 records of potential patients which includes 783 occurrences of stroke. The false negative rate from our prediction approach is only 19.1%, which has reduced by an average of 51.5% in comparison to other traditional approaches. The false positive rate, accuracy and sensitivity predicted by the proposed approach are respectively 33.1, 71.6, and 67.4%.Conclusion The approach proposed in this paper has effectively reduced the false negative rate with a relatively high overall accuracy, which means a successful decrease in the misdiagnosis rate for stroke prediction. The results are more reliable and valid as the reference in stroke prognosis, and also can be acquired conveniently at a low cost.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
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?
In the citing paper
“And, indeed, AI-powered systems have been shown to produce overall lower error rates than traditional methods [19–21].”
From Explainability for artificial intelligence in healthcare: a multidisciplinary perspective (2020) · cites this paper as [21]
Citations
Cites
The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138
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
Focal Loss for Dense Object Detection
Lin, T.-Y., Goyal, P., Girshick, R., He, K., & Dollar, P. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.324 · Cited by 30,581
Focal Loss for Dense Object Detection
Lin, T.-Y., Goyal, P., Girshick, R., He, K., & Dollar, P. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2858826 · Cited by 9,556
The Nature of Statistical Learning Theory
Sain, S. R., & Vapnik, V. N. (1996) · Technometrics · doi:10.2307/1271324 · Cited by 5,528
Naghavi, M., Abajobir, A. A., Abbafati, C., Abbas, K. M., Abd-Allah, F., Abera, S. F., Aboyans, V., Adetokunboh, O., Afshin, A., Agrawal, A., et al. (2017) · The Lancet · doi:10.1016/s0140-6736(17)32152-9 · Cited by 4,821
A study of the behavior of several methods for balancing machine learning training data
Batista, G. E. A. P. A., Prati, R. C., & Monard, M. C. (2004) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1007730.1007735 · Cited by 4,446
Galar, M., Fernández, A., Barrenechea, E., Bustince, H., & Herrera, F. (2011) · IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) · doi:10.1109/tsmcc.2011.2161285 · Cited by 2,877
Learning from class-imbalanced data: Review of methods and applications
Haixiang, G., Li, Y., Shang, J. S., Gu, M., Huang, Y., & Bing, G. (2016) · Expert Systems with Applications · doi:10.1016/j.eswa.2016.12.035 · Cited by 2,508
O'Donnell, M. J., Chin, S. L., Rangarajan, S., Xavier, D., Liu, L., Zhang, H., Rao-Melacini, P., Zhang, X., Pais, P., Agapay, S., et al. (2016) · The Lancet · doi:10.1016/s0140-6736(16)30506-2 · Cited by 2,296
Chawla, N. V., Japkowicz, N., & Kotcz, A. (2004) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1007730.1007733 · Cited by 2,245
Feigin, V. L., Norrving, B., & Mensah, G. A. (2017) · Circulation Research · doi:10.1161/circresaha.116.308413 · Cited by 2,184
Prasad, A. M., Iverson, L. R., & Liaw, A. (2006) · Ecosystems · doi:10.1007/s10021-005-0054-1 · Cited by 1,987
Data Mining and Knowledge Discovery Handbook
Maimon, O., & Rokach, L. (2005) · Springer eBooks · doi:10.1007/b107408 · Cited by 1,559
K -means clustering via principal component analysis
Ding, C. H. Q., & He, X. (2004) · doi:10.1145/1015330.1015408 · Cited by 1,412
(2015) · neural information processing systems · Cited by 1,305
A Survey of Predictive Modeling on Imbalanced Domains
Branco, P., Torgo, L., & Ribeiro, R. P. (2017) · ACM Computing Surveys · doi:10.1145/2907070 · Cited by 1,129
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Elder, J. F., Soulié, F. F., Flach, P., & Zaki, M. J. (2009) · doi:10.1145/1557019 · Cited by 1,124
Learning to Reweight Examples for Robust Deep LearningPreprint
Ren, M., Zeng, W., Yang, B., & Urtasun, R. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.09050 · Cited by 1,120
Proceedings of the twenty-first international conference on Machine learning
Brodley, C. E. (2004) · Cited by 1,020
Clustering-based undersampling in class-imbalanced data
Lin, W.-C., Tsai, C.-F., Hu, Y.-H., & Jhang, J.-S. (2017) · Information Sciences · doi:10.1016/j.ins.2017.05.008 · Cited by 832
Stroke in China: epidemiology, prevention, and management strategies.
Liu, M., Wu, B., Wang, W.-Z., Lee, L.-M., Zhang, S.-H., & Kong, L. (2007) · Lancet Neurology · doi:10.1016/s1474-4422(07)70004-2 · Cited by 733
Jerez, J. M., Molina, I., García-Laencina, P. J., Alba, E., Ribelles, N., Martín, M., & Franco, L. (2010) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2010.05.002 · Cited by 608
Data Mining in Healthcare and Biomedicine: A Survey of the Literature
Yoo, I., Alafaireet, P. E., Marinov, M. D., Pena-Hernandez, K. E., Gopidi, R., Chang, J.-F., & Hua, L. (2011) · Journal of Medical Systems · doi:10.1007/s10916-011-9710-5 · Cited by 570
Under-sampling class imbalanced datasets by combining clustering analysis and instance selection
Tsai, C., Lin, W., Hu, Y., & Yao, G.-T. (2018) · Information Sciences · doi:10.1016/j.ins.2018.10.029 · Cited by 338
Ben‐Gal, I. (2006) · Springer eBooks · doi:10.1007/0-387-25465-x_7 · Cited by 324
An integrated machine learning approach to stroke prediction
Khosla, A., Cao, Y., Lin, C. C.-Y., Chiu, H.-K., Hu, J., & Lee, H. (2010) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1835804.1835830 · Cited by 203
Richter, A. N., & Khoshgoftaar, T. M. (2018) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2018.06.002 · Cited by 164
Kasabov, N. K., Feigin, V. L., Hou, Z., Chen, Y., Liang, L., Krishnamurthi, R., Othman, M., & Parmar, P. G. K. (2014) · Neurocomputing · doi:10.1016/j.neucom.2013.09.049 · Cited by 149
A survey on computer-assisted Parkinson's Disease diagnosis
Pereira, C. R., Pereira, D. R., Weber, S. A. T., Hook, C., de Albuquerque, V. H. C., & Papa, J. P. (2018) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2018.08.007 · Cited by 143
Yu, H., Mu, C., Sun, C., Yang, W., Yang, X., & Zuo, X. (2014) · Knowledge-Based Systems · doi:10.1016/j.knosys.2014.12.007 · Cited by 112
Different medical data mining approaches based prediction of ischemic stroke
Arslan, A. K., Çolak, C., & Sarihan, M. E. (2016) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2016.03.022 · Cited by 99
Fuzzy-Based Information Decomposition for Incomplete and Imbalanced Data Learning
Liu, S., Zhang, J., Xiang, Y., & Zhou, W. (2017) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2017.2754998 · Cited by 81
Machine Intelligence in Healthcare and Medical Cyber Physical Systems: A Survey
Shishvan, O. R., Zois, D., & Soyata, T. (2018) · IEEE Access · doi:10.1109/access.2018.2866049 · Cited by 73
Robust support vector machine with generalized quantile loss for classification and regression
Yang, L., & Dong, H. (2019) · Applied Soft Computing · doi:10.1016/j.asoc.2019.105483 · Cited by 67
Application of knowledge discovery process on the prediction of stroke
Çolak, C., Karaman, E., & Turtay, M. G. (2015) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2015.03.002 · Cited by 59
Deep Learning and Missing Data in Engineering Systems
Leke, C. A., & Marwala, T. (2018) · Studies in big data · doi:10.1007/978-3-030-01180-2 · Cited by 56
Cascaded classifiers and stacking methods for classification of pulmonary nodule characteristics
Kaya, A. (2018) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2018.10.009 · Cited by 34
Online cost-sensitive neural network classifiers for non-stationary and imbalanced data streams
Ghazikhani, A., Monsefi, R., & Yazdi, H. S. (2012) · Neural Computing and Applications · doi:10.1007/s00521-012-1071-6 · Cited by 32
A robust classification framework with mixture correntropy
Wang, Y., Yang, L., & Ren, Q. (2019) · Information Sciences · doi:10.1016/j.ins.2019.04.016 · Cited by 29
Mining rules from an incomplete dataset with a high missing rate
Hong, T., & Wu, C.-W. (2010) · Expert Systems with Applications · doi:10.1016/j.eswa.2010.09.054 · Cited by 21
Self-citationA robust loss function for classification with imbalanced datasets
Wang, Y., & Yang, L. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.11.024 · Cited by 21
Stroke is treatable, but prevention is the key
Algra, A., & Wermer, M. J. H. (2017) · Nature Reviews Neurology · doi:10.1038/nrneurol.2017.4 · Cited by 19
Robust Bayesian Classification with Incomplete Data
Zhang, X., Song, S., & Wu, C. (2012) · Cognitive Computation · doi:10.1007/s12559-012-9188-6 · Cited by 16
Self-citationAn Optimized k-NN Approach for Classification on Imbalanced Datasets with Missing Data
Ozan, E. C., Riabchenko, E., Serkan Kiranyaz, B., & Gabbouj, M. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46349-0_34 · Cited by 15
Introduction to Missing Data Estimation
Leke, C. A., & Marwala, T. (2018) · Studies in big data · doi:10.1007/978-3-030-01180-2_1 · Cited by 8
Research works printed but not linked: 1
- Kodinariya, 2013, Review on determining number of cluster in k-means clustering, International J, 1, 90
Cited by
Sorted by citations
Explainability for artificial intelligence in healthcare: a multidisciplinary perspective
the Precise4Q consortium, Amann, J., Blasimme, A., Vayena, E., Frey, D., & Madai, V. I. (2020) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-020-01332-6 · Cited by 2,203
Lalmuanawma, S., Hussain, J., & Chhakchhuak, L. (2020) · Chaos, Solitons & Fractals · doi:10.1016/j.chaos.2020.110059 · Cited by 845
Lambert, S. I., Madi, M., Sopka, S., Lenes, A., Stange, H., Buszello, C.-P., & Stephan, A. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00852-5 · Cited by 404
Handling imbalanced medical datasets: review of a decade of research
Salmi, M., Atif, D., Oliva, D. A., Abraham, A., & Ventura, S. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10884-2 · Cited by 373
Machine Learning for Brain Stroke: A Review
Sirsat, M. S., Fermé, E., & Câmara, J. (2020) · Journal of Stroke and Cerebrovascular Diseases · doi:10.1016/j.jstrokecerebrovasdis.2020.105162 · Cited by 301
Metadata
- Authors
- Liu, T., Fan, W., & Wu, C.
- Affiliations
- Tsinghua University (CN)
- Corresponding author
- Fan, Wenhui
- Venue
- Artificial Intelligence in Medicine
- Publisher
- Elsevier BV
- Published
- 2019-11
- Volume
- 101
- Pages
- 101723
- ISSN
- 0933-3657 (ISSN-L)
- OpenAlex
- W2981817161
- Cited by
- 220
- References
- 49
- Citations by year
- 2026: 14 · 2025: 35 · 2024: 63 · 2023: 39 · 2022: 37 · 2021: 23 · 2020: 9
- FWCI
- 18.78
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Artificial Intelligence in Medicine (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Imbalanced Data Classification Techniques
- Health Sciences › Medicine › Clinical Neurology › Acute Ischemic Stroke Management
- Keywords
- class imbalance, missing data imputation, random forest regression, automated hyperparameter optimization, deep neural network, physiological parameters, false negative reduction, stroke prognosis
- MeSH terms
- Machine Learning, Algorithms, Humans, Prognosis, Neural Networks, Computer, Stroke, Datasets as Topic
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