Current critical review on prediction stroke using machine learning

Muhammad Modi Lakulu

QR Code Link :
Type :	Article
Subject :	T Technology (General)
ISBN :	2089-3191
Main Author :	Muhammad Modi Lakulu
Title :	Current critical review on prediction stroke using machine learning
Hits :	51

Place of Production :	Tanjung Malim
Publisher :	Fakulti Komputeran & Meta-Teknologi
Year of Publication :	2024
Notes :	Bulletin of Electrical Engineering and Informatics
Corporate Name :	Universiti Pendidikan Sultan Idris
HTTP Link :	Click to view web link
PDF Full Text :	You have no permission to view this item.

Abstract : Universiti Pendidikan Sultan Idris

Strokes are a significant health problem because they often lead to long-term disabilities due to delayed diagnoses and insufficient information about the disease. The use of artificial intelligence (AI), specifically machine learning (ML) and deep learning (DL), has the potential to aid in stroke diagnosis and significantly advance healthcare. This review article critically examines predictive methods for ischemic and hemorrhagic strokes. The preferred reporting items for systematic reviews and meta-analyses (PRISMA) method was used to identify 79 relevant articles from five databases spanning 2012 to 2022, with IEEE having the highest number of articles and citations. China had the most authors, and the random forest (RF) algorithm showed the most accurate results. A taxonomy categorizing the implementation and usage of ML and DL for stroke prediction was created and includes five focus areas: building, system planning, evaluation, comparison, and analysis. Additional research into other disease features related to stroke is warranted. Decentralized federated learning should also be implemented to collect data from remote locations for early diagnosis and create a single training model. _ 2024, Institute of Advanced Engineering and Science. All rights reserved.

References

J. S. Kim, "Stroke in Asia: a global disaster," International Journal of Stroke, vol. 9, no. 7, pp. 856-857, Oct. 2014, doi: 10.1111/ijs.12317.

N. Venketasubramanian, B. W. Yoon, J. Pandian, and J. C. Navarro, "Stroke epidemiology in south, east, and south-east asia: A review," Journal of Stroke, vol. 19, no. 3, pp. 286-294, Sep. 2017, doi: 10.5853/jos.2017.00234.

C. Abbafati et al., "Global burden of 369 diseases and injuries in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019," The Lancet, vol. 396, no. 10258, pp. 1204-1222, Oct. 2020, doi: 10.1016/S0140-6736(20)30925-9.

G. Geetha and K. M. Prasad, "The strategies to reduce healthcare costs and increase the quality of healthcare service delivery using hybrid technologies," in 2021 Asian Conference on Innovation in Technology, IEEE, Aug. 2021, pp. 1-7, doi: 10.1109/ASIANCON51346.2021.9544947.

Syarfaini, Nildawati, S. Aeni, Surahmawati, A. S. Adha, and M. Amansyah, "Risk factors preparation of stroke incidence in health institution employees who check up at the Health Service EXPO Event Indonesia," Gaceta Sanitaria, vol. 35, pp. S49-S52, 2021, doi: 10.1016/j.gaceta.2020.12.014.

A. H. Alamoodi et al., "A systematic review into the assessment of medical apps: motivations, challenges, recommendations and methodological aspect," Health and Technology, vol. 10, no. 5, pp. 1045-1061, Sep. 2020, doi: 10.1007/s12553-020-00451-4.

S. M. Samuri, T. V. Nova, Bahbibirahmatullah, W. S. Li, and Z. T. Al-Qaysi, "Classification Model for Breast Cancer Mammograms," IIUM Engineering Journal, vol. 23, no. 1, pp. 187-199, Jan. 2022, doi: 10.31436/IIUMEJ.V23I1.1825.

M. Anand Kumar, K. C. Purohit, and A. Singh, "Strokes-related disease prediction using machine learning classifiers and deep belief network model," in Lecture Notes in Electrical Engineering, 2023, pp. 143-155, doi: 10.1007/978-981-19-8493-8_11.

H. J. Lee, E. K. Choi, S. H. Lee, Y. J. Kim, K. Do Han, and S. Oh, "Risk of ischemic stroke in metabolically healthy obesity: A nationwide population-based study," PLoS ONE, vol. 13, no. 3, p. e0195210, Mar. 2018, doi: 10.1371/journal.pone.0195210.

P. Papadimitroulas et al., "Artificial intelligence: deep learning in oncological radiomics and challenges of interpretability and data harmonization," Physica Medica, vol. 83, pp. 108-121, Mar. 2021, doi: 10.1016/j.ejmp.2021.03.009.

N. Darabi, N. Hosseinichimeh, A. Noto, R. Zand, and V. Abedi, "Machine learning-enabled 30-day readmission model for stroke patients," Frontiers in Neurology, vol. 12, Mar. 2021, doi: 10.3389/fneur.2021.638267.

J. Hamann et al., "Machine-learning-based outcome prediction in stroke patients with middle cerebral artery-M1 occlusions and early thrombectomy," European Journal of Neurology, vol. 28, no. 4, pp. 1234-1243, Apr. 2021, doi: 10.1111/ene.14651.

H. Asadi, R. Dowling, B. Yan, and P. Mitchell, "Machine learning for outcome prediction of acute ischemic stroke post intra-arterial therapy," PLoS ONE, vol. 9, no. 2, p. e88225, Feb. 2014, doi: 10.1371/journal.pone.0088225.

I. Mishra and S. Mohapatra, "An enhanced approach for analyzing the performance of heart stroke prediction with machine learning techniques," International Journal of Information Technology (Singapore), vol. 15, no. 6, pp. 3257-3270, Aug. 2023, doi: 10.1007/s41870-023-01321-8.

M. S. Singh and P. Choudhary, "Stroke prediction using artificial intelligence," in 2017 8th Industrial Automation and Electromechanical Engineering Conference, IEEE, Aug. 2017, pp. 158-161, doi: 10.1109/IEMECON.2017.8079581.

W. Ji, C. Wang, H. Chen, Y. Liang, and S. Wang, "Predicting post-stroke cognitive impairment using machine learning: A prospective cohort study," Journal of Stroke and Cerebrovascular Diseases, vol. 32, no. 11, p. 107354, Nov. 2023, doi: 10.1016/j.jstrokecerebrovasdis.2023.107354.

Y. Iwamoto et al., "Development and validation of machine learning-based prediction for dependence in the activities of daily living after stroke inpatient rehabilitation: a decision-tree analysis," Journal of Stroke and Cerebrovascular Diseases, vol. 29, no. 12, p. 105332, Dec. 2020, doi: 10.1016/j.jstrokecerebrovasdis.2020.105332.

S. Bhattacharya et al., "A novel PCA-firefly based XGBoost classification model for intrusion detection in networks using GPU," Electronics (Switzerland), vol. 9, no. 2, p. 219, Jan. 2020, doi: 10.3390/electronics9020219.

S. Wang et al., "A machine learning strategy for fast prediction of cardiac function based on peripheral pulse wave," Computer Methods and Programs in Biomedicine, vol. 216, p. 106664, Apr. 2022, doi: 10.1016/j.cmpb.2022.106664.

H. Zhang et al., "Pharmacodynamic advantages and characteristics of traditional Chinese medicine in prevention and treatment of ischemic stroke," Chinese Herbal Medicines, vol. 15, no. 4, pp. 496-508, Oct. 2023, doi: 10.1016/j.chmed.2023.09.003.

A. R. A. Taleb, M. Hoque, A. Hasanat, and M. B. Khan, "Application of data mining techniques to predict length of stay of stroke patients," in 2017 International Conference on Informatics, Health and Technology, IEEE, Feb. 2017, pp. 1-5, doi: 10.1109/ICIHT.2017.7899004.

K. Mouridsen, P. Thurner, and G. Zaharchuk, "Artificial intelligence applications in stroke," Stroke, vol. 51, no. 8, pp. 2573-2579, Aug. 2020, doi: 10.1161/STROKEAHA.119.027479.

Y. Liang, X. Zheng, and D. D. Zeng, "A survey on big data-driven digital phenotyping of mental health," Information Fusion, vol. 52, pp. 290-307, Dec. 2019, doi: 10.1016/j.inffus.2019.04.001.

This material may be protected under Copyright Act which governs the making of photocopies or reproductions of copyrighted materials.
You may use the digitized material for private study, scholarship, or research.

Back to search page