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Stroke Risk Stratification Using Neural Networks

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Intelligent Sustainable Systems (WorldS4 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 812))

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Abstract

Current primary stroke preventive strategies seem insufficient in light of the increased prevalence of stroke, the steady or increasing death rates from cardiovascular illnesses, and the expanding list of stroke risk factors. A class of computer algorithms known as machine learning (ML) can learn from data without having to be explicitly programmed. To predict stroke and its effects, a number of physiological and clinical indicators have been used. A cyber-physical stroke rehabilitation system (CP-SRS) as well as the modified Rankin Scale (mRS90) and National Institutes of Health Stroke Scale (NIHSS24) have both been predicted using ANN models. The results of this study indicate that neural networks might create a new and efficient way to categorize stroke patients’ risk.

Author Contributions

SP conceived the concepts, planned, designed, wrote, and edited the manuscript.

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Competing Interests

The authors declare that they have no competing interests.

Funding Source

This study was supported from Pilot Funded Summer Seed Research Grant (2023), “Utilization of Machine Learning Techniques for Aiding Detection of Ischemic Stroke Lesion, Infarct Volumes, and Small-artery Occlusions”, Claflin University, 400 Magnolia St, Orangeburg, SC 29115.

Supplementary Files

The data utilized can be downloaded from here: https://github.com/spawar2/Neural-Networks-Stroke/blob/main/pone.0231113.s002.xlsx

The training code can be accessed here: https://github.com/spawar2/Neural-Networks-Stroke/blob/main/Neural-Networks-Stroke.R

Author information

Authors and Affiliations

  1. Department of Computer Science, Claflin University, Orangeburg, SC, 29115, USA

    Shrikant Pawar

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  1. Shrikant Pawar
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Corresponding author

Correspondence to Shrikant Pawar .

Editor information

Editors and Affiliations

  1. School of Mathematics, Computer Science and Engineering, Liverpool Hope University, Liverpool, UK

    Atulya K. Nagar

  2. Namibia University of Science and Technology, Windhoek, Namibia

    Dharm Singh Jat

  3. Symbiosis University of Applied Sciences, Indore, India

    Durgesh Mishra

  4. Global Knowledge Research Foundation, Ahmedabad, India

    Amit Joshi

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Pawar, S. (2024). Stroke Risk Stratification Using Neural Networks. In: Nagar, A.K., Jat, D.S., Mishra, D., Joshi, A. (eds) Intelligent Sustainable Systems. WorldS4 2023. Lecture Notes in Networks and Systems, vol 812. Springer, Singapore. https://doi.org/10.1007/978-981-99-8031-4_3

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