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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AI-powered systems have been shown to produce overall lower error rates than traditional methods.
“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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MethodAn Improved MAHAKIL Oversampling Method for Imbalanced Dataset Classification
Zhang, Y., Zuo, T., Fang, L., Li, J., & Xing, Z. (2020) · IEEE Access · doi:10.1109/access.2020.3047741 · Cited by 18 · see citing works
“It is widely existed in fields such as illegal network intrusion [1], disease medical diagnosis [2], spam recognition [3] and so on.”
BackgroundEnhancing Brain Disease Diagnosis with XAI: A Review of Recent Studies
Bibi, N., Courtney, J., & McGuinness, K. (2025) · ACM Transactions on Computing for Healthcare · doi:10.1145/3709152 · Cited by 17 · see citing works
Analysis of Uneven Stroke Prediction Dataset using Machine Learning
Uttam, A. K. (2022) · International Conference on Intelligent Computing and Control Systems (ICICCS) · doi:10.1109/iciccs53718.2022.9788309 · Cited by 17 · see citing works
Balancing cerebrovascular disease data with integrated ensemble learning and SVM-SMOTE
Nithya, R., Kokilavani, T., & Beena, T. L. A. (2024) · Network Modeling Analysis in Health Informatics and Bioinformatics · doi:10.1007/s13721-024-00447-4 · Cited by 17 · see citing works
“Liu et al. (2019) developed a mixed machine-learning technique for cerebral stroke clinical diagnosis.”
Ahmed, R., Varshney, A., Ashraf, Z., Farooqui, N. A., & Pathak, R. S. (2024) · SN Computer Science · doi:10.1007/s42979-024-03389-w · Cited by 17 · see citing works
“In the studies [26–29], the deep neural network [30] has been used in the methodology along with two other methods of supervised ML algorithms; logistic regression and RF; some have achieved good results with the gradient boosting technique as well many research, including this one, have…”
Venaik, A., Kumari, R., Venaik, U., & Nayyar, A. (2022) · International Journal of Reliable and Quality E-Healthcare · doi:10.4018/ijrqeh.2022010107 · Cited by 16 · see citing works
Weng, X., Song, H., Lin, Y., Wu, Y., Zhang, X., Liu, B., & Yang, J. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107687 · Cited by 16 · see citing works
Machine learning-based patient classification system for adults with stroke: A systematic review
Ruksakulpiwat, S., Thongking, W., Zhou, W., Benjasirisan, C., Phianhasin, L., Schiltz, N. K., & Brahmbhatt, S. (2021) · Chronic Illness · doi:10.1177/17423953211067435 · Cited by 15 · see citing works
“16 The optimal result showed that the model ’ s false-negative rate is”
BackgroundMethodInfluentialLombardi, A., Arezzo, F., Sciascio, E. D., Ardito, C. A., Mongelli, M., Lillo, N. D., Fascilla, F. D., Silvestris, E., Kardhashi, A., Putino, C., et al. (2023) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2023.102697 · Cited by 15 · see citing works
Hasan, N., & Bao, Y. (2021) · Health and Technology · doi:10.1007/s12553-021-00538-6 · Cited by 16 · see citing works
Discrepancies in Stroke Distribution and Dataset Origin in Machine Learning for Stroke
Velagapudi, L., Mouchtouris, N., Baldassari, M. P., Nauheim, D. O., Khanna, O., Saiegh, F. A., Herial, N. A., Gooch, M. R., Tjoumakaris, S. I., Rosenwasser, R. H., et al. (2021) · Journal of Stroke and Cerebrovascular Diseases · doi:10.1016/j.jstrokecerebrovasdis.2021.105832 · Cited by 15 · see citing works
Identifying stroke-related quantified evidence from electronic health records in real-world studies
Yang, L., Huang, X., Wang, J., Yang, X., Ding, L., Li, Z., & Li, J. (2023) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2023.102552 · Cited by 15 · see citing works
FL-PSO: A Federated Learning approach with Particle Swarm Optimization for Brain Stroke Prediction
Victor, N., Bhattacharya, S., Maddikunta, P. K. R., Alotaibi, F. M., Gadekallu, T. R., & Jhaveri, R. H. (2023) · doi:10.1109/ccgridw59191.2023.00020 · Cited by 15 · see citing works
“…machines (SVM) to predict the chances of SICH and was further compared with established prognostication tools to evaluate its performance in validating the same, The study in [18] developed a hybrid machine learning framework considering physiological data for the prediction of stroke disease.”
MethodLavanya, J. M. S., & Subbulakshmi, P. (2024) · Scientific Reports · doi:10.1038/s41598-024-70354-1 · Cited by 14 · see citing works
“An algorithm for stroke prediction has been developed by Singh et al. 17 and compared to a variety of other methods on the dataset “Cardiovascular Health Study (CHS)”.”
MethodRetractedArtificial Bee Colony Optimized Deep Neural Network Model for Handling Imbalanced Stroke Data
Dev, A., & Malik, S. K. (2021) · International Journal of E-Health and Medical Communications · doi:10.4018/ijehmc.20210901.oa5 · Cited by 14 · see citing works
“Liu et al. (Liu et al., 2019) adopted two methods for predicting the cerebral stroke based on the physiological data.”
MethodHuang, J., & Liu, W. (2025) · Medicine · doi:10.1097/md.0000000000042690 · Cited by 13 · see citing works
“[6,7] However, there is still room for improvement in accuracy and calibration of these models at present.”
Chang, H., Zhang, H., Shi, G., Guo, J., Chu, X., Wang, Z., Yao, Y., & Wang, X. (2023) · Brain and Behavior · doi:10.1002/brb3.3307 · Cited by 13 · see citing works
“…widespread use of big data, artificial intelligence has deduced excellent predictive value in predicting stroke risk with the excellent characteristics of ML in automating the decision-making process (Hung et al., 2017; Khosla et al., 2010; Leung et al., 2018; Liu et al., 2019; Weng et al., 2017).”
BackgroundVinisha, F. A., & Sujihelen, L. (2022) · International Conference on Smart Systems and Inventive Technology (ICSSIT) · doi:10.1109/icssit53264.2022.9716355 · Cited by 13 · see citing works
“This approach predicts cerebral stroke and thus reduces the false-negative rate at a low cost [15].”
BackgroundBrain Stroke Classification using One Dimensional Convolutional Neural Network
Kifli, N. R., Hidayat, H., Rahmawati, R., Sukoco, F. P., Yuniarti, A. R., & Rizal, S. (2022) · IEEE Asia Pacific Conference on Wireless and Mobile (APWiMob) · doi:10.1109/apwimob56856.2022.10014207 · Cited by 13 · see citing works
Prediction of Stroke Disease Using Different Types of Gradient Boosting Classifiers
Pradhan, A. K., Swain, S., Rout, J. K., & Ray, N. K. (2022) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-981-16-8403-6_30 · Cited by 12 · see citing works
Stroke classification based on deep reinforcement learning over stroke screening imbalanced data
Zuo, T., Li, F., Zhang, X., Hu, F., Huang, L., & Jia, W. (2024) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2023.109069 · Cited by 12 · see citing works
Liu, C., Lee, M., Hsueh, S. J., Chung, C.-C., Lin, C., Chang, P.-H., Luo, A.-C., Weng, H.-C., Lee, Y.-H., Dai, M.-J., et al. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108244 · Cited by 12 · see citing works
Distributed Heterogeneous Transfer Learning
Mignone, P., Pio, G., & Ceci, M. (2024) · Big Data Research · doi:10.1016/j.bdr.2024.100456 · Cited by 12 · see citing works
Mohapatra, U. M., & Tripathy, S. (2023) · IAES International Journal of Artificial Intelligence · doi:10.11591/ijai.v13.i1.pp487-499 · Cited by 11 · see citing works
“Deep learning [30], [31] methods have been adopted in different domains for the task of object detection, image segmentation, image classification and so on.”
Stroke Prediction Using Deep Learning and Transfer Learning Approaches
Shih, D., Wu, Y.-H., Wu, T.-W., Chu, H.-Y., & Shih, M. (2024) · IEEE Access · doi:10.1109/access.2024.3429157 · Cited by 11 · see citing works
“Therefore, it is very important to properly deal with the category imbalance data [10], [15].”
BackgroundMethodInfluentialYang, F., Qiao, Y., Hájek, P., & Abedin, M. Z. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124886 · Cited by 11 · see citing works
Machine learning to predict stroke risk from routine hospital data: A systematic review
Heseltine-Carp, W., Courtman, M., Browning, D., Kasabe, A. M., Allen, M. J., Streeter, A., Ifeachor, E., James, M. A., & Mullin, S. (2025) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2025.105811 · Cited by 17 · see citing works
“Deep neural networks (DNN) also yielded a higher AUC and out-performed other ML models in 5 of 8 cases [21,31,53,54,61].”
Liu, T., Wang, Y., Yang, Y., Sun, M., Fan, W., Bünger, C. E., & Wu, C. (2022) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2021.102235 · Cited by 12 · see citing works
Self-citationApplicability of classifier to discovery knowledge for future prediction modelling
Chauhan, R., & Yafi, E. (2022) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-022-03694-3 · Cited by 10 · see citing works
“In a decision tree, the formation of a model depends on data which can easily adapted to the variables, which include offsets, unbalanced effects, nested effects, noise of the data-sets, etc. (Vaka et al. 2020; Liu et al. 2019).”
BackgroundMitra, R., & Rajendran, T. (2022) · Advances in parallel computing · doi:10.3233/apc220075 · Cited by 10 · see citing works
Stroke Risk Prediction and Prevention: Traditional versus Machine Learning Approaches
Singh, M. S., Thongam, K., Choudhary, P., & Bhagat, P. K. (2025) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-025-10406-5 · Cited by 11 · see citing works
Mualfah, D., Fadila, W. S. N., & Firdaus, R. (2022) · Jurnal CoSciTech (Computer Science and Information Technology) · doi:10.37859/coscitech.v3i2.3912 · Cited by 9 · see citing works
“Pengumpulan Data Data pada penelitian ini diperoleh dari penelitian sebelumnya[7].”
BackgroundA Transfer Learning-Based Approach to Detect Cerebral Microbleeds
Alrumiah, S. S., & Al-Shargabi, A. A. (2021) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2022.021930 · Cited by 9 · see citing works
GlyphGenius: Unleashing the Potential of AIGC in Chinese Character Learning
Wang, L., Lou, Y., Li, X., Xiang, Y., Jiang, T., Che, Y., & Ye, C. (2024) · IEEE Access · doi:10.1109/access.2024.3464562 · Cited by 9 · see citing works
“The SVG files are a large dataset of Chinese characters generated in advance from TTF files [44], [45], [46].”
MethodSingh, R., Kaur, S., Singh, G., Kaur, M., & Kaur, P. (2024) · Scientific Reports · doi:10.1038/s41598-024-80129-3 · Cited by 8 · see citing works
“Their research includes performance analysis of SVM with established prognostication tools like SEDAN and HAT scores 6 implemented a hybrid model to anticipate stroke.”
Application of Ensemble Methods in Medical Diagnosis
Shree, R., Madagaonkar, S. B., Prateek, L. A., Tony, A. E., Rathnamma, M. V., Ramana, V. V., & Chandrasekaran, K. (2023) · Advances in intelligent systems and computing · doi:10.1007/978-981-99-0550-8_29 · Cited by 8 · see citing works
Advithi, D., & Umadevi, V. (2024) · International Conference on Emerging Technologies in Computer Science for Interdisciplinary Applications (ICETCS) · doi:10.1109/icetcs61022.2024.10544182 · Cited by 8 · see citing works
“The study presented in [5] utilized a dataset of 43,400 patient records, which included 783 stroke cases.”
MethodMulti-label rhinitis prediction using ensemble neural network chain with pre-training
Yang, J., Zhang, M., Liu, P., & Yu, S. (2022) · Applied Soft Computing · doi:10.1016/j.asoc.2022.108839 · Cited by 8 · see citing works
Tahyudin, I., Nurhopipah, A., Tikaningsih, A., Lestari, P., Suryana, Y., Winarko, E., Winarto, E., Haza, N., & Nambo, H. (2025) · Computer Methods and Programs in Biomedicine Update · doi:10.1016/j.cmpbup.2025.100198 · Cited by 7 · see citing works
RetractedVariational autoencoder‐guided data augmentation for imbalanced medical diagnostics
Al-zahrani, M. S., Alsaade, F. W., & Alsaadi, F. E. (2025) · Results in Engineering · doi:10.1016/j.rineng.2025.107011 · Cited by 7 · see citing works
Hassan, S. M., Ali, S. A., Hassan, B., Husain, I., Rafiq, M., & Awan, S. A. (2022) · International Journal of Biology and Biomedical Engineering · doi:10.46300/91011.2022.16.20 · Cited by 7 · see citing works
“The RF algorithm is successfully used to identify the missing values before classification and then to predict stroke on an imbalanced dataset by automated hyperparameter optimization (Auto-HPO), based on DNN [24],[25].”
Mondal, S., Choudhary, P., & Rathee, P. (2025) · Engineering Research Express · doi:10.1088/2631-8695/adad3a · Cited by 7 · see citing works
Bbosa, F. F., Nabukenya, J., Nabende, P., & Wesonga, R. (2021) · Health and Technology · doi:10.1007/s12553-021-00551-9 · Cited by 7 · see citing works
“This may possibly be due to the presence of imbalanced data, given that it’s a key challenge to achieving successful estimates from predictive data mining algorithms [24, 35].”
BackgroundPrediction of Brain Stroke using Machine Learning with Relief Algorithm
Jayashree, K., & Prakash, N. K. (2022) · International Conference on Edge Computing and Applications (ICECAA) · doi:10.1109/icecaa55415.2022.9936142 · Cited by 7 · see citing works
“A machine learning algorithm, a notification system, and the usage of the FOG median can all assist to decrease the severity of an emergency [15], [16].”
MethodBrain Stroke Prediction Using Machine Learning Techniques
Almubark, I. (2023) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata59044.2023.10386474 · Cited by 7 · see citing works
“Some of them lack good model interpretability, which makes it even harder to integrate ML into the clinical setting [10-14].”
BackgroundStroke Prediction Using Machine Learning Algorithms
Kanwal, N., Javaid, S., & Dewi, D. D. (2025) · International Global Conference Series on ICT Integration in Technical Education & Smart Society · doi:10.3390/engproc2025107032 · Cited by 6 · see citing works
Early stroke behavior detection based on improved video masked autoencoders for potential patients
Wang, M., Yang, G., Luo, K., Li, Y., & He, L. (2024) · Complex & Intelligent Systems · doi:10.1007/s40747-024-01610-0 · Cited by 6 · see citing works
“It has been shown that recognizing early stroke behaviors through image recognition algorithms speeds up the treatment decision-making process and saves patients’ lives [9].”
Fusing Attention and Convolution: A Hybrid Model for Brain Stroke Prediction
Bhuvanya, R., Kujani, T., & Sivakumar, K. (2024) · ICST Transactions on Scalable Information Systems · doi:10.4108/eetsis.7022 · Cited by 6 · see citing works
Fulazzaky, T., Saefuddin, A., & Soleh, A. M. (2024) · Scientific Journal of Informatics · doi:10.15294/sji.v11i4.15937 · Cited by 6 · see citing works
A Comparative Study of ML Algorithms for Scenario-agnostic Predictions in Healthcare
Mavrogiorgou, A., Kleftakis, S., Zafeiropoulos, N., Mavrogiorgos, K., Kiourtis, A., & Kyriazis, D. (2022) · IEEE Symposium on Computers and Communications (ISCC) · doi:10.1109/iscc55528.2022.9912808 · Cited by 6 · see citing works
“Therefore, it is desirable to observe a high percentage in all the three (3) targets, whereas the deviation rates should be around 1%-2% [53].”
BackgroundAnalysis of AI Driven Brain Stroke Prediction Using Machine Learning and Deep Learning
Mandhare, R. M., & Kshirsagar, D. B. (2024) · International Conference on Communication, Computer Sciences and Engineering (IC3SE) · doi:10.1109/ic3se62002.2024.10593413 · Cited by 6 · see citing works
Liu, T., Yang, Y., Wang, Y., Sun, M., Fan, W., Wu, C., & Bünger, C. E. (2020) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/3410530.3414317 · Cited by 6 · see citing works
Self-citationEnhancing Stroke Prediction Using LightGBM With SMOTE-ENN and Fine-Tuning: A Comprehensive Analysis
Swain, K., Tak, T. K., Upreti, K., Kshirsagar, P. R., Krishnan, S. B., Poonia, R. C., Nayak, S. R., & Mohanty, M. N. (2024) · Cureus Journal of Computer Science. · doi:10.7759/s44389-024-02268-y · Cited by 5 · see citing works
A cluster-assisted differential evolution-based hybrid oversampling method for imbalanced datasets
Karabıyık, M. A., Türkoğlu, B., & Aşuroğlu, T. (2025) · PeerJ Computer Science · doi:10.7717/peerj-cs.3177 · Cited by 5 · see citing works
- 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 › Epidemiology › 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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