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.
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“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]
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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
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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
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