Predicting Adherence to Canada’s Food Guide Recommendations on Healthy Food Choices Using Machine Learning Algorithms
Bibliographic record
Abstract
Machine learning (ML) algorithms can potentially improve predictive performances compared to traditional statistical models. The aim of this study was to predict adherence to the 2019 Canada's Food Guide (CFG) recommendations on healthy food choices using ML and a large array of variables/features related to dietary habits. In a sample of 1147 French-speaking adults (50% women) from the PREDISE study, Healthy Eating Food Index (HEFI-2019) scores were calculated using data from three unannounced web-based 24h recalls. Adherence to the 2019 CFG recommendations on healthy food choices (yes or no) was measured with the HEFI-2019 and arbitrarily defined as a score ≥46.7/80 points. This value corresponds to the median HEFI-2019 score for adult women in Canada. A total of 2452 features encompassing individual, social and environmental characteristics related to dietary habits were retained as predictors in the analyses. Decision tree (DT) and Adaboost ML algorithms were developed, calibrated and then compared using accuracy score (proportion of correct predictions), precision score (positive predictive value) and recall score (sensitivity). All analytical steps were bootstrapped 100 times to generate 95%CI. The most important features retained by each ML algorithm were compared. The DT predicted adherence to the 2019 CFG recommendations on healthy food choices with an accuracy of 0.65 (95%CI: 0.59–0.71), a precision of 0.64 (95%CI: 0.44–0.84) and a recall of 0.31 (95%CI: 0.10–0.52). Adaboost had similar predictive performance metrics with an accuracy of 0.64 (95%CI: 0.59–0.69), a precision of 0.56 (95%CI: 0.45–0.67) and a recall of 0.49 (95%CI: 0.39–0.59). However, among the 15 most important features retained by each ML algorithm, only 6 features (40%) were shared by both. The use of DT and Adaboost ML algorithms does not predict adherence to the 2019 CFG recommendations on healthy food choices measured by the HEFI-2019 score with high accuracy. The inconsistencies in the features retained by each ML algorithm also suggest that results are model-dependent. Further research is therefore necessary to successfully implement ML approaches that may help better predict adherence to dietary recommendations such as those found in the 2019 CFG. Instituts de la recherche en santé du Canada, Fonds de recherche du Québec - Santé.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".