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Artificial Neural Networks for RTW Outcome Prediction in Malaysia’s Socso Program: A Semma-Based Predictive Analytics Study

Author

Listed:
  • M. Z. A. Chek

    (Actuarial Science Department, UiTM Perak Branch)

  • I. L. Ismail

    (Department of Statistics and Decision Science, UiTM Perak Branch)

  • E. N. I. Hashim

    (Actuarial Science Department, UiTM N. Sembilan Branch)

  • Z. H. Zulkifli

    (Actuarial Partners Consulting, Malaysia)

  • Muhammad Syakir Asrulsani

    (Actuarial Science Department, UiTM Perak Branch)

  • Rinda Nariswari

    (Department of Computer Science, BINUS Indonesia)

Abstract

Return-to-Work (RTW) programmes administered by the Social Security Organization of Malaysia (SOCSO) are critical in facilitating the reintegration of injured or ill employees into productive employment. However, accurately predicting rehabilitation outcomes remains challenging due to the complex and nonlinear interactions among demographic and employment-related factors. This study develops a predictive modelling framework using Artificial Neural Networks (ANN) to enhance outcome forecasting within SOCSO’s RTW programme.

Suggested Citation

  • M. Z. A. Chek & I. L. Ismail & E. N. I. Hashim & Z. H. Zulkifli & Muhammad Syakir Asrulsani & Rinda Nariswari, 2026. "Artificial Neural Networks for RTW Outcome Prediction in Malaysia’s Socso Program: A Semma-Based Predictive Analytics Study," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 10(2), pages 8625-8633, February.
    • Handle: RePEc:bcp:journl:v:10:y:2026:i:2:p:8625-8633
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    References listed on IDEAS

    as
    1. M.Z.A. Chek & I.L. Ismail, 2021. "Issues and Challenges Social Insurance in Malaysia," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 5(4), pages 278-281, April.
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