Dependency‑Light ML Forecasts of ASEAN Smoking Prevalence with Uncertainty & Policy Scenarios (1990‑2035)

<ns3:p>Background Within the ASEAN region, smoking is one of the leading preventable causes of death, contributing to over 10% of all global smoking-related deaths. Accurate policy modelling and effective forecasting are therefore vital to support strategies that aim to control tobacco regionally. Methods Our study created an installation-free, open-source machine learning model that could predict smoking prevalence and simulate the potential effect of a 15% tobacco tax rise within the 11 ASEAN countries. This was done by clustering countries based on historical prevalence using dynamic time warping k-means (DTW-kM). The performance of the model was then compared with the traditional autoregressive integrated moving average (ARIMA) approach, which used mean absolute error (MAE) as the primary accuracy metric. Results Compared to the traditional ARIMA, the stacked long short term memory (LSTM) model performed better in forecasting accuracy (median MAE 0.32 vs 0.46 percentage points, p &lt; 0.01). If no intervention is made, the smoking prevalence across the ASEAN region will fall from 26.35 (2022) to 24.6% by 2035. Implementing a 15% tax rise in 2026 improves this percentage to 22.0% which averts 1.3 million disability-adjusted life years (DALYs). Conclusion This openly accessible tool therefore supports evidence-based tobacco policy and provides a reproducible, data-driven approach to forecast tobacco use trends and evaluate the effect of policy interventions.</ns3:p>