Prospects and Pitfalls of Machine Learning in Nutritional Epidemiology
Nutritional epidemiology employs observational data to discover associations between diet and disease risk. However, existing analytic methods of dietary data are often sub-optimal, with limited incorporation and analysis of the correlations between the studied variables and nonlinear behaviours in...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-04-01
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| Series: | Nutrients |
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| Online Access: | https://www.mdpi.com/2072-6643/14/9/1705 |
| _version_ | 1797503477343059968 |
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| author | Stefania Russo Stefano Bonassi |
| author_facet | Stefania Russo Stefano Bonassi |
| author_sort | Stefania Russo |
| collection | DOAJ |
| description | Nutritional epidemiology employs observational data to discover associations between diet and disease risk. However, existing analytic methods of dietary data are often sub-optimal, with limited incorporation and analysis of the correlations between the studied variables and nonlinear behaviours in the data. Machine learning (ML) is an area of artificial intelligence that has the potential to improve modelling of nonlinear associations and confounding which are found in nutritional data. These opportunities notwithstanding, the applications of ML in nutritional epidemiology must be approached cautiously to safeguard the scientific quality of the results and provide accurate interpretations. Given the complex scenario around ML, judicious application of such tools is necessary to offer nutritional epidemiology a novel analytical resource for dietary measurement and assessment and a tool to model the complexity of dietary intake and its relation to health. This work describes the applications of ML in nutritional epidemiology and provides guidelines to avoid common pitfalls encountered in applying predictive statistical models to nutritional data. Furthermore, it helps unfamiliar readers better assess the significance of their results and provides new possible future directions in the field of ML in nutritional epidemiology. |
| first_indexed | 2024-03-10T03:51:15Z |
| format | Article |
| id | doaj.art-ed8be73bdfa149dcb6fb8e11dac77257 |
| institution | Directory Open Access Journal |
| issn | 2072-6643 |
| language | English |
| last_indexed | 2024-03-10T03:51:15Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Nutrients |
| spelling | doaj.art-ed8be73bdfa149dcb6fb8e11dac772572023-11-23T08:57:15ZengMDPI AGNutrients2072-66432022-04-01149170510.3390/nu14091705Prospects and Pitfalls of Machine Learning in Nutritional EpidemiologyStefania Russo0Stefano Bonassi1EcoVision Lab, Photogrammetry and Remote Sensing Group, ETH Zürich, 8092 Zurich, SwitzerlandDepartment of Human Sciences and Quality of Life Promotion, San Raffaele University, 00166 Rome, ItalyNutritional epidemiology employs observational data to discover associations between diet and disease risk. However, existing analytic methods of dietary data are often sub-optimal, with limited incorporation and analysis of the correlations between the studied variables and nonlinear behaviours in the data. Machine learning (ML) is an area of artificial intelligence that has the potential to improve modelling of nonlinear associations and confounding which are found in nutritional data. These opportunities notwithstanding, the applications of ML in nutritional epidemiology must be approached cautiously to safeguard the scientific quality of the results and provide accurate interpretations. Given the complex scenario around ML, judicious application of such tools is necessary to offer nutritional epidemiology a novel analytical resource for dietary measurement and assessment and a tool to model the complexity of dietary intake and its relation to health. This work describes the applications of ML in nutritional epidemiology and provides guidelines to avoid common pitfalls encountered in applying predictive statistical models to nutritional data. Furthermore, it helps unfamiliar readers better assess the significance of their results and provides new possible future directions in the field of ML in nutritional epidemiology.https://www.mdpi.com/2072-6643/14/9/1705nutritional epidemiologyartificial intelligencemachine learningmodelling |
| spellingShingle | Stefania Russo Stefano Bonassi Prospects and Pitfalls of Machine Learning in Nutritional Epidemiology Nutrients nutritional epidemiology artificial intelligence machine learning modelling |
| title | Prospects and Pitfalls of Machine Learning in Nutritional Epidemiology |
| title_full | Prospects and Pitfalls of Machine Learning in Nutritional Epidemiology |
| title_fullStr | Prospects and Pitfalls of Machine Learning in Nutritional Epidemiology |
| title_full_unstemmed | Prospects and Pitfalls of Machine Learning in Nutritional Epidemiology |
| title_short | Prospects and Pitfalls of Machine Learning in Nutritional Epidemiology |
| title_sort | prospects and pitfalls of machine learning in nutritional epidemiology |
| topic | nutritional epidemiology artificial intelligence machine learning modelling |
| url | https://www.mdpi.com/2072-6643/14/9/1705 |
| work_keys_str_mv | AT stefaniarusso prospectsandpitfallsofmachinelearninginnutritionalepidemiology AT stefanobonassi prospectsandpitfallsofmachinelearninginnutritionalepidemiology |