AAAI Publications, Workshops at the Thirty-Second AAAI Conference on Artificial Intelligence

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Using Digital Purchasing Data to Generate Public Health Evidence: Learning Unhealthy Beverage Demand from Grocery Transaction Data
Hiroshi Mamiya, Xing Han Lu, Yu Ma, David L. Buckeridge

Last modified: 2018-06-20

Abstract


Unhealthy diet plays a major role in driving chronic disease incidence and prevalence. Taxation of unhealthy food has been proposed to improve population-level dietary patterns, and its effectiveness can be estimated by the prediction of the change in unhealthy food purchasing upon increase of food price. Recent availability of grocery transaction data from scanner technologies enables an accurate prediction of food sales. However, the very large number of product at-tributes in these data prohibits the application of conventional statistical learning algorithms. In this study, we explored the predictive performance of learning algorithms adapted for high-dimensional data, namely the Least Absolute Shrinkage and Selection Operator (LASSO) and Decision Tree Regressor with Adaptive Boosting (DTR-Ada-Boost), in comparison with a conventional statistical learning based on Ordinary Least Square (OLS). LASSO demonstrated superior predictive accuracy to OLS, possibly due to its ability to reduce over fitting and collinearity across predictive features of food sales. DTR-AdaBoost showed the best predictive accuracy, suggesting the presence of extensive non-linearity between the predictive features in the transaction data and sales.

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