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Open AccessArticle

Nutrient Estimation from 24-Hour Food Recalls Using Machine Learning and Database Mapping: A Case Study with Lactose

1
Western Human Nutrition Research Center, USDA ARS, Davis, CA 95616, USA
2
Genome Center, University of California Davis, Davis, CA 95616, USA
3
Department of Mechanical Engineering, University of California Davis, Davis, CA 95616, USA
4
Department of Nutrition, University of California Davis, Davis, CA 95616, USA
5
Department of Computer Science, University of California Davis, Davis, CA 95616, USA
*
Author to whom correspondence should be addressed.
Nutrients 2019, 11(12), 3045; https://doi.org/10.3390/nu11123045
Received: 30 October 2019 / Revised: 30 November 2019 / Accepted: 6 December 2019 / Published: 13 December 2019
The Automated Self-Administered 24-Hour Dietary Assessment Tool (ASA24) is a free dietary recall system that outputs fewer nutrients than the Nutrition Data System for Research (NDSR). NDSR uses the Nutrition Coordinating Center (NCC) Food and Nutrient Database, both of which require a license. Manual lookup of ASA24 foods into NDSR is time-consuming but currently the only way to acquire NCC-exclusive nutrients. Using lactose as an example, we evaluated machine learning and database matching methods to estimate this NCC-exclusive nutrient from ASA24 reports. ASA24-reported foods were manually looked up into NDSR to obtain lactose estimates and split into training (n = 378) and test (n = 189) datasets. Nine machine learning models were developed to predict lactose from the nutrients common between ASA24 and the NCC database. Database matching algorithms were developed to match NCC foods to an ASA24 food using only nutrients (“Nutrient-Only”) or the nutrient and food descriptions (“Nutrient + Text”). For both methods, the lactose values were compared to the manual curation. Among machine learning models, the XGB-Regressor model performed best on held-out test data (R2 = 0.33). For the database matching method, Nutrient + Text matching yielded the best lactose estimates (R2 = 0.76), a vast improvement over the status quo of no estimate. These results suggest that computational methods can successfully estimate an NCC-exclusive nutrient for foods reported in ASA24. View Full-Text
Keywords: dietary recall; nutrient database; machine learning; database matching dietary recall; nutrient database; machine learning; database matching
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Chin, E.L.; Simmons, G.; Bouzid, Y.Y.; Kan, A.; Burnett, D.J.; Tagkopoulos, I.; Lemay, D.G. Nutrient Estimation from 24-Hour Food Recalls Using Machine Learning and Database Mapping: A Case Study with Lactose. Nutrients 2019, 11, 3045.

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