Precision nutrition: A systematic literature review

Precision Nutrition research aims to use personal information about individuals or groups of deliver nutritional advice that, theoretically, would be more suitable than generic advice. Machine learning, a subbranch Artificial Intelligence, has promise aid in the development predictive models that are for Nutrition. As such, recent applied machine learning algorithms, tools, and techniques precision nutrition different purposes. However, systematic overview state-of-the-art on is lacking. Therefore, we carried out Systematic Literature Review (SLR) provide an where how been used from various aspects, what such as input features, availability status data literature is, evaluated. Nine questions were defined this study. We retrieved 4930 papers electronic databases 60 primary studies selected respond questions. All also briefly discussed article. Our results show fifteen problems spread across seven domains health present. Four tasks seen form regression, classification, recommendation clustering, with most these utilizing supervised approach. In total, 30 algorithms used, 19 appearing once. Models through four approaches 23 evaluation metrics. Personalized promising reduce burden current research, review shows Learning can incorporated into high performance. researchers should consider incorporating their methods facilitate integration many complex allowing high-performance approaches.