Exploring Dietary Intake Data collected by FPQ using Unsupervised Learning

Gjoreski, Martin; Kochev, Stefan; Reščič, Nina; Gregoric, Matej; Eftimov, Tome; Seljak, Barbara Koroušić

doi:10.1109/bigdata47090.2019.9005715

Cited by 3 publications

(2 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The use of machine learning seems an excellent option to work with existing, already validated questionnaires and adapt them to different needs and not have an expert involved to design the questionnaires by hand, but rather use expert knowledge to supervise and validate the machine-learning outcomes. However, to the best of our knowledge, machine learning has mainly been used just to estimate nutrient intake or to detect dietary patterns [ 8 , 9 , 10 ]. Dimensionality reduction methods such as Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) have been used in order to detect correlations between different food groups [ 11 ] also involving data gathered from FFQs [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

Food Frequency Questionnaire Personalisation Using Multi-Target Regression

et al. 2022

View full text Add to dashboard Cite

Fondazione Bruno Kessler is developing a mobile app prototype for empowering citizens to improve their health conditions through different lifestyle interventions that will be incorporated into a mobile application for lifestyle promotion of the Province of Trento in the context of the Trentino Salute 4.0 Competence Center. The envisioned interventions are based on promoting behaviour change in various domains such as physical activity, mental health and nutrition. In particular, the nutrition component is a self-monitoring module that collects dietary habits to analyse them and recommend healthier eating behaviours. Dietary assessment is completed using a Food Frequency Questionnaire on the Mediterranean diet that is presented to the user as a grid of images. The questionnaire returns feedback on 11 aspects of nutrition. Although the questionnaire used in the application only consists of 24 questions, it still could be a bit overwhelming and a bit crowded when shown on the screen. In this paper, we tried to find a machine-learning-based solution to reduce the number of questions in the questionnaire. We proposed a method that uses the user’s previous answers as additional information to find the goals that need more attention. We compared this method with a case where the subset of questions is randomly selected and with a case where the subset is chosen using feature selection. We also explored how large the subset should be to obtain good predictions. All the experiments are conducted as a multi-target regression problem, which means several goals are predicted simultaneously. The proposed method adjusts well to the user in question and has the slightest error when predicting the goals.

show abstract

Section: Introductionmentioning

confidence: 99%

Food Frequency Questionnaire Personalisation Using Multi-Target Regression

et al. 2022

View full text Add to dashboard Cite

show abstract

“…They showed that machine learning was superior to statistical methods and that it could be a valuable tool in the field of nutritional epidemiology. Machine-learning methods have also been used to detect dietary patterns [ 7 , 8 ] and to estimate nutrient values [ 9 ]; however, to the best of our knowledge no one has used machine learning to help experts find an optimal subset of FFQ questions without losing too much information.…”

Section: Introductionmentioning

confidence: 99%

Optimising an FFQ Using a Machine Learning Pipeline to Teach an Efficient Nutrient Intake Predictive Model

et al. 2020

Self Cite

View full text Add to dashboard Cite

Food frequency questionnaires (FFQs) are the most commonly selected tools in nutrition monitoring, as they are inexpensive, easily implemented and provide useful information regarding dietary intake. They are usually carefully drafted by experts from nutritional and/or medical fields and can be validated by using other dietary monitoring techniques. FFQs can get very extensive, which could indicate that some of the questions are less significant than others and could be omitted without losing too much information. In this paper, machine learning is used to explore how reducing the number of questions affects the predicted nutrient values and diet quality score. The paper addresses the problem of removing redundant questions and finding the best subset of questions in the Extended Short Form Food Frequency Questionnaire (ESFFFQ), developed as part of the H2020 project WellCo. Eight common machine-learning algorithms were compared on different subsets of questions by using the PROMETHEE method, which compares methods and subsets via multiple performance measures. According to the results, for some of the targets, specifically sugar intake, fiber intake and protein intake, a smaller subset of questions are sufficient to predict diet quality scores. Additionally, for smaller subsets of questions, machine-learning algorithms generally perform better than statistical methods for predicting intake and diet quality scores. The proposed method could therefore be useful for finding the most informative subsets of questions in other FFQs as well. This could help experts develop FFQs that provide the necessary information and are not overbearing for those answering.

show abstract