Cited by 220Artificial Intelligence in HealthcareImbalanced Data Classification TechniquesAcute Ischemic Stroke Management

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A hybrid machine learning approach to cerebral stroke prediction based on imbalanced medical dataset

Liu, Tianyu, Fan, Wenhui, Wu, Cheng·2019·Artificial Intelligence in Medicine

DOI 10.1016/j.artmed.2019.101723

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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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Influential
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Addressing Imbalanced Data in Stroke Prediction: An Oversampling Approach for Improved Accuracy
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A Metalearning Approach to Personalized Automatic Assessment of Rectilinear Sketches
Castro, L. M. C., Ray, S., Merzdorf, H., Douglas, K. A., & Hammond, T. (2021) · IEEE Frontiers in Education Conference (FIE) · doi:10.1109/fie49875.2021.9637255 · Cited by 1
“This algorithm has been used in multiple ﬁelds such as image recognition [22], medicine [23] and biology [24], but to our knowledge, the current study is the ﬁrst in which this algorithm is applied in an education context.”
Method
Physiological Status Prediction Based on a Novel Hybrid Intelligent Scheme
Liu, N., Ma, C., Xu, M., Ge, Y., & Gan, D. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/4610747 · Cited by 1
“[22] A hybrid machine learning approach to cerebral stroke prediction based on an imbalanced medical dataset Firstly, random forest regression is adopted to impute missing values before classifcation.”
Method
Development and Validation of Interpretable Machine Learning for Stroke Occurrence in Older, Community Chinese DwellersPreprint
Wu, Y., Jiang, Z., Lin, S., & Fang, Y. (2021) · Research Square · doi:10.21203/rs.3.rs-604690/v1
“So far, most studies focused on stroke diagnosis [7-11], which required complex medical data, such as physical, neurological, and brain imaging examination (CT or MRI) to exclude other diseases (stroke mimics), and to recognize its type, location and severity [12].”
Background
RETRACTED: A hybrid cost-sensitive machine learning approach for the classification of intelligent disease diagnosis
Chen, X., Jin, W., Wu, Q., Zhang, W., & Liang, H. (2022) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-213486
“Liu et al. [30] used a method combining RF regression and deep neural networks (DNN) to construct a model for the classiﬁcation of cerebral stroke in clinical diagnosis.”
Retracted
Development and Validation of Interpretable Machine Learning Approaches for Early Identification of Stroke in Older, Community Dwellers (Preprint)Preprint
Wu, Y., Jiang, Z., Lin, S., & Fang, Y. (2021) · doi:10.2196/preprints.28182
Interpretable prediction for in-hospital adverse stroke events with feature engineering (Preprint)Preprint
Zhang, S., Li, R., Wang, N., Song, B., Dai, H., & Xu, Y. (2021) · doi:10.2196/preprints.28631
Topical Repute on Artificial Intelligence-Based Approaches in COVID-19 Supervision
Basu, S., & Shafi, J. (2022) · Advances in medical technologies and clinical practice book series · doi:10.4018/978-1-6684-3791-9.ch008
Machine Learning Prediction of Stroke Occurrence: A Systematic Review (Preprint)Preprint
Lolak, S., Supplasilp, C., Poprom, N., Sapankaew, T., Yin, M. S., Thammasudjarit, R., McKay, G. J., Haddawy, P., Attia, J., & Thakkinstian, A. (2022) · doi:10.2196/preprints.38554
Machine Learning Approaches to Stroke Prediction Based on the Framingham Cardiovascular Study Dataset
Chirindza, J., & Ajoodha, R. (2022) · Algorithms for intelligent systems · doi:10.1007/978-981-19-3951-8_36
High-precision blood glucose prediction and hypoglycemia warning based on the LSTM-GRU model
Peng, X., Li, Q., Yannian, W., & Yan, D. (2022) · Trends in Computer Science and Information Technology · doi:10.17352/tcsit.000053
“Hybrid blood glucose prediction models [12] combine the two previous approaches together.”
Method
Evaluating Biomedical Features for Reactive Hypoglycaemia Prediction with Machine Learning
Dany, F., & Retiaty, F. (2023) · doi:10.2991/978-94-6463-112-8_11
Development of a flexible self-calculation scoring model to determine stroke occurrence
Kyeong, S.-H., & Kim, D. H. (2023) · Journal Of Big Data · doi:10.1186/s40537-023-00785-6
“18% of the whole original dataset [15].”
Method
STNet: shape and texture joint learning through two-stream network for knowledge-guided image recognition
Wang, X.-J., Han, H., Xu, M., Li, S., Zhang, D., Du, S., & Xu, M. (2023) · Frontiers in Neuroscience · doi:10.3389/fnins.2023.1212049
“Due to the characteristics of the disease, training data often contains more benign lesions than malignant ones, resulting in insuﬃcient attention given to malignant samples during network training and negatively impacting model optimization (Liu et al., 2019a) and (Xu et al., 2020).”
Background
SFC-SMOTE: A Sample Filtering Clustering Oversampling Algorithm for Imbalanced Clinical Diagnostic Data
Xu, Z., Li, W., Yang, F., Wang, H., Sun, J., Wang, S., & Zhang, Y. (2026) · Journal of Imaging Science and Technology · doi:10.2352/j.imagingsci.technol.2026.70.6.060501
การศกษาตวจำแนกประเภทการเรยนรของเครองเพอทำนายโรคหลอดเลอดสมอง
อ้ายดี, ฐ. (2021) · doi:10.58837/chula.the.2021.903
Web Design for Stroke Early Detection Using Decision Tree C5.0
Purwanti, E., Nor, R. U. N. U., & Soelistyono, S. (2023) · Komputasi Jurnal Ilmiah Ilmu Komputer dan Matematika · doi:10.33751/komputasi.v20i2.8265
Biased Cv Static Correlation Based XGB-SVM Stack Model for Stroke PredictionPreprint
Kanna, P. R. (2023) · Research Square · doi:10.21203/rs.3.rs-3461792/v1
Explainable Machine Learning Methods for Person-Based Prediction in Simulated and Real Datasets: Methodological Research
Kar, İ., & Bakırarar, B. (2023) · Turkiye Klinikleri Journal of Biostatistics · doi:10.5336/biostatic.2023-99050
Analysis and modification of mutual information-based feature selection methods regarding data imbalance and incompleteness
Li, K. (2023) · doi:10.17760/d20487166
Stroke Risk Stratification Using Neural Networks
Pawar, S. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-99-8031-4_3
Machine Learning Prediction of Stroke Occurrence: A Systematic ReviewPreprint
Lolak, S., Suppasilp, C., Poprom, N., Sapankaew, T., Yin, M. S., Thammasudjarit, R., McKay, G. J., Attia, J., & Thakkinstian, A. (2024) · medRxiv · doi:10.1101/2024.03.28.24305014
Prediction of rhinitis with class imbalance based on heterogeneous ensemble learning
Yang, J., Jiang, B., Qiu, Z., Meng, Y., Zhang, X., Yu, S., Dai, F., & Qian, Y. (2024) · Computer Methods in Biomechanics & Biomedical Engineering · doi:10.1080/10255842.2024.2339461
“In addition, Tianyu Liu proposed a novel deep neural network models (DNN) (Liu et al. 2019), AutoHPO, to cope with the class imbalance for medical dataset.”
Design and Development of a Hybrid Evolutionary Method with a Special Selection of Artificial Immune System for Stroke Prediction: A Balancing Approach
Çelikbaş, Ş., Orman, Z., & Akgündoğdu, A. (2025) · Computing and Informatics · doi:10.31577/cai_2025_5_1101
“Building upon the work of Liu et al.(2019) 15 , Santos et al.(2022) 16 addressed the missing data in the preprocessing step by deleting it.”
BackgroundMethodInfluential
Dengesiz Veri Kümelerinde İnme Tahmini İçin Özel Seçilimli Hibrit Dengeleme Yöntemi Tasarımı ve Uygulaması
Çelikbaş, Ş., Orman, Z., Aksoy, T., & Baysoy, D. Y. (2024) · Düzce Üniversitesi Bilim ve Teknoloji Dergisi · doi:10.29130/dubited.1268348
Artificial intelligence-based detection of stroke in non contrast computed tomography images
(2024) · International Research Journal of Modernization in Engineering Technology and Science · doi:10.56726/irjmets60799
Integrated KIFCM Clustering Technique for Brain Stroke Segmentation
Papana, K. R., Bhavanam, S. N., & Devi, M. V. (2024) · International Conference on Image Processing and Capsule Networks (ICIPCN) · doi:10.1109/icipcn63822.2024.00029
A review on COVID-19 (SARS-CoV-2) pandemic: Using artificial intelligence and machine learning applications
Kalgi, J. R., & Nalbalwar, S. L. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-22038-8.00007-6
Predicting abnormal C-reactive protein level for improving utilization by deep neural network model
Mo, D., Xiong, S., Ji, T., Zhou, Q., & Zheng, Q. (2024) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2024.105726
Predictive Modeling of Cerebral Strokes: An ADASYN-RF Approach for Imbalanced Data
Aish, M. A. (2024) · VFAST Transactions on Software Engineering · doi:10.21015/vtse.v12i4.1932
Comparison of Various Computational Models for Accurate Stroke Prediction and Interpretation Using LIME and SHAP Kernels
Arun, C. B., Anusha, M. P., Kodipalli, A., Gargi, N., Harish, L., & Devamane, S. B. (2024) · IEEE International Conference for Women in Computing (InCoWoCo) · doi:10.1109/incowoco64194.2024.10863346
FOG Grounded Observing Scheme for E-Healthcare Based Stroke Estimation and Alert Message in IoT Environment
Pandey, S., & Mishra, R. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-69115-7_26
Crunch Mode: Make Early Predictions about Risk of Stroke Using Machine Learning
Truong, V.-P., Lee, D., & Huh, J. (2025) · Journal of information and communication convergence engineering · doi:10.56977/jicce.2025.23.1.17
Examining the Accuracy of Machine Learning Classification Models in Stroke Prediction
Naskar, M. N. B. J., Prakash, A., Sinha, V., Kumari, H., & Bhattacharjee, A. (2025) · Smart innovation, systems and technologies · doi:10.1007/978-981-96-1348-9_28
Kdebo: A Kernel Density Estimation-Guided Differential Evolution-Based Oversampling Technique for Imbalanced LearningPreprint
Türkoğlu, B. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5247184
Predicting Cerebral Stroke: Comparison Between Anomaly Detection Algorithms and Classification Models
Sarkar, R., Gupta, V., Gupta, P., Hiwrale, S., Patil, A., Jadhav, A. S., & Dallal, J. A. (2025) · International Conference on IT Innovation and Knowledge Discovery (ITIKD) · doi:10.1109/itikd63574.2025.11004700
“Among the many contributions to this topic one of them is a hybrid form of ML approach for predicting cerebral stroke on an imbalanced medical dataset, where missing values are filled using Random Forest Regression [9].”
Correlation Analysis of Postpartum Hemorrhage with Labor Analgesia Based on Machine Learning
Yuan, X., Wen, H., Li, F., Lin, Q., Deng, Z., Huang, X., Huang, X., & Zhang, Y. (2023) · International Conference on Networking Systems of AI (INSAI) · doi:10.1109/insai60116.2023.00053
“Moreover, a machine learning algorithm was applied to predict low blood pressure [7,8].”
Biclustering-KNN joint learning in anomaly detection for handling class-imbalance-problem
Lu, Z., Wei, H., Ye, F., & Huang, Q. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.113991

Authors: Liu, T., Fan, W., & Wu, C.
Affiliations: Tsinghua University (CN)
Corresponding author: Fan, Wenhui
ORCID: Liu, Tianyu
Fan, Wenhui
Wu, Cheng
Venue: Artificial Intelligence in Medicine
Publisher: Elsevier BV
Published: 2019-11
Volume: 101
Pages: 101723
ISSN: 0933-3657 (ISSN-L)
DOI: 10.1016/j.artmed.2019.101723
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
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
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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A hybrid machine learning approach to cerebral stroke prediction based on imbalanced medical dataset

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