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ML_SPS: Stroke Prediction System Employing Machine Learning Approach

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Artificial Intelligence and Data Science (ICAIDS 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1673))

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Abstract

Stroke is a dangerous health issue that happens when bleeding valves in the brain get damaged. and blood supply to the brain is cut off. As a result of these factors, numerous body parts may cease to function. Stroke is currently a significant risk factor for mortality, as per World Health Organization (WHO). It may be preferable to mitigate the severity of stroke by detecting it early. In recent years, data science has been critical to the growth of research in the medical field. Various machine learning techniques are built employing a patient’s physical and physiological reporting data to forecast the risk of stroke. In this article, we use five machine learning approaches to find the best effective model that can predict the risk of stroke, including Decision Tree (DT), XGBoost, Light Gradient Boosting Machine (LGBM), Random Forest (RF), and K-nearest Neighbors learning. Kaggle was used to collect the dataset. Random forest produces acceptable findings with an accuracy of roughly 96%, which might be included in actual clinical information. The machine learning approach can aid in the prediction of many diseases like a stroke in the early stages.

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Acknowledgement

The study was supported by funding from the Natural Sciences and Engineering Research Council of Canada (NSERC).

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Daffodil International University, Sukrabad, Dhaka-1207, Bangladesh

    Md. Sazzad Hossain & Mehedi Hassan Shovo

  2. Department of Software Engineering, Daffodil International University, Sukrabad, Dhaka-1207, Bangladesh

    Md. Mamun Ali

  3. Department of Computer Science and Engineering, B.I.T. Patna Campus, Samanpura, BIT Patna, Bihar, 800014, India

    Md. Nayer

  4. Group of Bio-Photomatix, Department of Information and Communication Technology, Mawlana Bhashani Science and Technology University, Tangail, 1902, Bangladesh

    Kawsar Ahmed

  5. Department of Electrical and Computer Engineering, University of Saskatchewan, 57 Campus 9 Drive, Saskatoon, SK, S7N 5A9, Canada

    Kawsar Ahmed & Francis M. Bui

Authors
  1. Md. Sazzad Hossain
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  2. Mehedi Hassan Shovo
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  3. Md. Mamun Ali
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  4. Md. Nayer
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  5. Kawsar Ahmed
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  6. Francis M. Bui
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Corresponding author

Correspondence to Kawsar Ahmed .

Editor information

Editors and Affiliations

  1. Sreyas Institute of Engineering and Technology, Hyderabad, India

    Ashwani Kumar

  2. University of Maribor, Maribor, Slovenia

    Iztok Fister Jr.

  3. Jaypee University of Information Technology, Solan, India

    P. K. Gupta

  4. LGF CNRS UMR 5307, MINES, Saint-Etienne, France

    Johan Debayle

  5. University of North Florida, Jacksonville, FL, USA

    Zuopeng Justin Zhang

  6. King Khalid University, Abha, Saudi Arabia

    Mohammed Usman

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Hossain, M.S., Shovo, M.H., Ali, M.M., Nayer, M., Ahmed, K., Bui, F.M. (2022). ML_SPS: Stroke Prediction System Employing Machine Learning Approach. In: Kumar, A., Fister Jr., I., Gupta, P.K., Debayle, J., Zhang, Z.J., Usman, M. (eds) Artificial Intelligence and Data Science. ICAIDS 2021. Communications in Computer and Information Science, vol 1673. Springer, Cham. https://doi.org/10.1007/978-3-031-21385-4_19

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  • DOI: https://doi.org/10.1007/978-3-031-21385-4_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-21384-7

  • Online ISBN: 978-3-031-21385-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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