Artificial Neural Networks for RTW Outcome Prediction in Malaysia’s Socso Program: A Semma-Based Predictive Analytics Study
Authors
Actuarial Science Department, UiTM Perak Branch (Malaysia)
Department of Statistics and Decision Science, UiTM Perak Branch (Malaysia)
Actuarial Science Department, UiTM N. Sembilan Branch (Malaysia)
Actuarial Partners Consulting, Malaysia (Malaysia)
Actuarial Science Department, UiTM Perak Branch (Malaysia)
Department of Computer Science, BINUS Indonesia (Malaysia)
Article Information
DOI: 10.47772/IJRISS.2026.10200611
Subject Category: Social science
Volume/Issue: 10/2 | Page No: 8625-8633
Publication Timeline
Submitted: 2026-02-25
Accepted: 2026-03-03
Published: 2026-03-23
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.
Keywords
Return to Work (RTW), Artificial Neural Networks (ANN), Predictive Analytics, SOCSO
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References
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