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
Ahmad et al. (Fri,) studied this question.