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Influencing Factors Analysis and Prediction Model Development of Stroke: The Machine Learning Approach

Author

Listed:
  • Juhua Wu

    (School of Management, Guangdong University of Technology, Guangzhou, P. R. China)

  • Qide Zhang

    (School of Management, Guangdong University of Technology, Guangzhou, P. R. China)

  • Lei Tao

    (School of Management, Guangdong University of Technology, Guangzhou, P. R. China)

  • Xiaoyun Lu

    (School of Management, Guangdong University of Technology, Guangzhou, P. R. China)

Abstract

Prediction is an important way to analyse stroke risk management. This study explored the critical influencing factors of stroke, used the classical multilayer perception (MLP) and radial basis function (RBF) machine learning (ML) algorithms to develop the model for stroke prediction. The two models were trained with Bagging and Boosting ensemble learning algorithms. The performances of the prediction models were also compared with other classical ML algorithms. The result showed that (1) total cholesterol (TC) and other nine factors were selected as principal factors for the stroke prediction; (2) the MLP model outperformed RBF model in terms of accuracy, generalization and inter-rater reliability; (3) ensemble algorithm was superior to single algorithms for high-dimension dataset in this study. It may come to the conclusion that this study improved the stroke prediction methods and contributed much to the prevention of stroke.

Suggested Citation

  • Juhua Wu & Qide Zhang & Lei Tao & Xiaoyun Lu, 2023. "Influencing Factors Analysis and Prediction Model Development of Stroke: The Machine Learning Approach," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 22(01), pages 1-16, February.
    • Handle: RePEc:wsi:jikmxx:v:22:y:2023:i:01:n:s0219649222500794
    DOI: 10.1142/S0219649222500794
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