Development of a Deep Learning Model for the Early Prediction of Stroke
DOI:
https://doi.org/10.65150/EP-jnsrr/V1E4/2025-02Keywords:
Stroke Prediction; Convolutional Neural Network (CNN); Deep Learning; Healthcare; Data Preprocessing; Feature ExtractionAbstract
This study presents the development of a deep learning model for the prediction of stroke using a healthcare dataset. The dataset was thoroughly preprocessed, encompassing feature extraction, data partitioning, and missing value management, to ensure optimal conditions for model training and evaluation. Regularisation techniques were included into a Convolutional Neural Network (CNN) architecture that was carefully designed with layers tuned for classification tasks in order to decrease overfitting and enhance generalisation. The model's performance was comprehensively assessed using a 10-fold cross-validation technique, which produced metrics such as accuracy, precision, recall, F1-score, and the Area Under the Curve (AUC). The findings showed that the CNN's average accuracy, precision, recall, and F1-score were 94.88%, 94.28%, and 95.15%, respectively. The calculated AUC of 0.96 revealed the model's high discriminating capacity. These results show the model's resilience and dependability across several data subsets, suggesting its potential for efficient stroke detection.
References
1) Abbasi, H., Orouskhani, M., Asgari, S., & Zadeh, S. (2023). Automatic brain ischemic stroke segmentation with deep learning: A review. Neuroscience Informatics, 3, 100145. https://doi.org/10.1016/j.neuri.2023.100145
2) Al-Mekhlafi, Z. G., Senan, E. M., Rassem, T. H., Mohammed, B. A., Makbol, N. M., Alanazi, A. A., Almurayziq, T. S., & Ghaleb, F. A. (2022). Deep learning and machine learning for early detection of stroke and haemorrhage. Computers, Materials & Continua, 72, 775–796. https://doi.org/[
3) Al-Mekhlafi, Z. G., Senan, E. M., Rassem, T. H., Mohammed, B. A., Makbol, N. M., Alanazi, A. A., Almurayziq, T. S., & Ghaleb, F. A. (2023). A hybrid stacked CNN and residual feedback GMDH-LSTM deep learning model for stroke prediction applied on mobile AI smart hospital platform. Sensors, 23, 3500. https://doi.org/[
4) Ashrafuzzaman, M., Saha, S., & Nur, K. (2023). Prediction of stroke disease using deep CNN-based approach. Journal of Advances in Information Technology, 13(6).
5) Bashir, U., Yap, J., & Rasuli, B. (2023). Diffusion-weighted imaging. Radiopaedia.org
6) Chen, Y. H., & Sawan, M. (2021). Trends and challenges of wearable multimodal technologies for stroke risk prediction. Sensors, 21(2), 460. https://doi.org/10.3390/s21020460
7) Donato, S., Caputo, S., Brombal, L., Golosio, B., Longo, R., Tromba, G., ... & Taba, S. T. (2024). Comparison of three reconstruction algorithms for low-dose phase-contrast computed tomography of the breast with synchrotron radiation. arXiv:2412.11641. Retrieved from https://arxiv.org/abs/2412.11641
8) Gao, C., & Wang, H. (2024). Intelligent stroke disease prediction model using deep learning approaches. Wiley Stroke Research and Treatment, Volume 2024, Article ID 4523388. https://doi.org/10.1155/2024/4523388
9) Ma, J., Sun, Y. Y., Liu, J. J., Huang, H. X., Zhou, X. S., & Xu, S. X. (2022). Multi-objective learning and explanation for stroke risk assessment in Shanxi province. Scientific Reports, 12(1), Article 22337. https://doi.org/10.1038/s41598-022-22337
10) Mackay, J., & Mensah E., (2014). The Atlas of Heart Disease and Stroke. World Health Organization.
11) Ozaltin, O., Coskun, O., Yeniay, O., & Subasi, A. (2022). A deep learning approach for detecting stroke from brain CT images using OzNet. Bioengineering, 9(12), 783. https://doi.org/10.3390/bioengineering9120783
12) Prasad, K. S., & Pasupathy, S. (2022). A machine learning framework with hybrid feature engineering for leveraging brain stroke detection performance. Mathematics, Statistics & Engineering Applications, 71, 1805–1824.
13) Purnell, B. A. (2014). Parenting. A legacy that transcends genes. Science, 345, 742-743. Retrieved from https://paperpile.com/s/magnetic-resonance-imaging-citation-style/
14) Qasrawi, R., Qdaih, I., Daraghmeh, O., Thwib, S., Vicuna Polo, S., Atari, S., & Abu Al-Halawa, D. (2024). Hybrid ensemble deep learning model for advancing ischemic brain stroke detection and classification in clinical application. Journal of Imaging, 10(7), Article 160. https://doi.org/10.3390/jimaging10070160
15) Smith, J., & Doe, J. (2020). Functional Magnetic Resonance Imaging (fMRI) and Behavioral Cognitive Tasks: A Comparative Study. Journal of Cognitive Neuroscience, 32(8), 1234-1256. Retrieved from https://www.studocu.com/en-us/messages/question/10591345/reference-apa-citation-functional-magnetic-resonance-imaging-fmri-and-behavioral-cognitive-tasks
16) Suganyadevi, S., Seethalakshmi, V., & Balasamy, K. (2022). A review on deep learning in medical image analysis. International Journal of Multimedia Information Retrieval, 11, 19–38. https://doi.org/10.1007/s13735-021-00218-1
17) Sushmitha, M., Kanthi, M., Kedlaya, V., Parupudi, T., Bhat, S. N., & Nayak, S. G. (2024). Identification of Vertebrae in CT Scans for Improved Clinical Outcomes Using Advanced Image Segmentation. Signals, 5(4), 869-882. https://www.mdpi.com/2624-6120/5/4/47
18) Thiyagarajan, S., Senthil, R., & Murugan, K. (2021). A systematic review on techniques adapted for segmentation and classification of ischemic stroke lesions from brain MR images. Wireless Personal Communications, 118, 1–19.https://doi.org/10.1007/s11277-021-08069-z
19) Yamashita, R., Nishio, M., Do, R. K. G., & Togashi, K. (2018). Convolutional neural networks: An overview and application in radiology. Insights into Imaging, 9, 611–629.
20) Zheng, J., Xiong, Y., Zheng, Y. M., Zhang, H. T., & Wu, R. (2024). Evaluating the stroke risk of patients using machine learning: A new perspective from Sichuan and Chongqing. Evaluation Review, 48(2), 346–369. https://doi.org/10.1177/37533403
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Asogwa Tochukwu Chijindu (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
