Application of Machine Learning and Grocery Transaction Data to Forecast Effectiveness of Beverage Taxation.

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ID: 77073
2019
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Abstract
Sugar Sweetened Beverages (SSB) are the primary source of artificially added sugar and have a casual association with chronic diseases. Taxation of SSB has been proposed, but limited evidence exists to guide this public health policy. Grocery transaction data, with price, discounting and other information for beverage products, present an opportunity to evaluate the likely effects of taxation policy. Sales are often non-linearly associated with price and are affected by the prices of multiple competing brands. We evaluated the predictive performance of Boosted Decision Tree Regression (B-DTR) and Deep Neural Networks (DNN) that account for the non-linearity and competition across brands, and compared their performance to a benchmark regression, the Least Absolute Shrinkage and Selection Operator (LASSO). B-DTR and DNN showed a lower Mean Squared Error (MSE) of prediction in the sales of most major SSB brands in comparison to LASSO, indicating a superior accuracy in predicting the effectiveness of SSB taxation. We demonstrated the application of machine learning methods and large transactional data from grocery stores to forecast the effectiveness food taxation.
Reference Key
lu2019applicationstudies Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Lu, Xing Han;Mamiya, Hiroshi;Vybihal, Joseph;Ma, Yu;Buckeridge, David L;
Journal Studies in health technology and informatics
Year 2019
DOI
10.3233/SHTI190221
URL
Keywords
Machine learning public policy beverages

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