MIND dataset for diet planning and dietary healthcare with machine learning: Dataset creation using combinatorial optimization and controllable generation with domain experts

Part of Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1 (NeurIPS Datasets and Benchmarks 2021)

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Authors

Changhun Lee, Soohyeok Kim, Sehwa Jeong, Chiehyeon Lim, Jayun Kim, Yeji Kim, Minyoung Jung

Abstract

Diet planning, a basic and regular human activity, is important to all individuals. Children, adults, the healthy, and the infirm all profit from diet planning. Many recent attempts have been made to develop machine learning (ML) applications related to diet planning. However, given the complexity and difficulty of implementing this task, no high-quality diet-level dataset exists at present. Professionals, particularly dietitians and physicians, would benefit greatly from such a dataset and ML application. In this work, we create and publish the Korean Menus–Ingredients–Nutrients–Diets (MIND) dataset for a ML application regarding diet planning and dietary health research. The nature of diet planning entails both explicit (nutrition) and implicit (composition) requirements. Thus, the MIND dataset was created by integrating input from experts who considered implicit data requirements for diet solution with the capabilities of an operations research (OR) model that specifies and applies explicit data requirements for diet solution and a controllable generative machine that automates the high-quality diet generation process. MIND consists of data from 1,500 South Korean daily diets, 3,238 menus, and 3,036 ingredients. MIND considers the daily recommended dietary intake of 14 major nutrients. MIND can be easily downloaded and analyzed using the Python package dietkit accessible via the package installer for Python. MIND is expected to contribute to the use of ML in solving medical, economic, and social problems associated with diet planning. Furthermore, our approach of integrating data from experts with OR and ML models is expected to promote the use of ML in other fields that require the generation of high-quality synthetic professional task data, especially since the use of ML to automate and support professional tasks has become a highly valuable service.