Developing and validating machine learning algorithms to predict various indices of diet quality among a socio-economically disadvantaged group

Abstract Public health research faces challenges in recruiting socio-economically disadvantaged groups. This study evaluated whether machine learning (ML) algorithms developed using data from a general population could predict indices of diet quality among a socio-economically disadvantaged group. Data from 5367 adults (77·5 % females) in the NutriQuébec project and on 122 variables potentially associated with dietary intakes were used. Dietary intakes were measured using a web-based 24-h recall. Participants were categorised by fifths of a deprivation score based on income, education and material and social deprivation. Participants in the first four fifths formed the general NutriQuébec sample ( n 4180) and those above the fifth quintile formed the high deprivation sample ( n 1187). Three indices of diet quality defined as ‘high’ or ‘low’ were used: vegetable and fruit consumption (VFC, ≥ 5·0 reference amounts (RA)/d), ‘other foods’ consumption, meaning, foods not recommended in Canada’s Food Guide 2019 (OFC, > 5·0 RA/d) and overall diet quality measured using the Healthy Eating Food Index-2019 (HEFI-2019, > 48·9 points). The algorithms developed and tested in the general NutriQuébec sample predicted high VFC, OFC and HEFI-2019 with accuracies of 0·60 (95 % CI 0·58, 0·62), 0·58 (95 % CI 0·56, 0·60) and 0·61 (95 % CI 0·59, 0·63), respectively. In the high deprivation sample, the algorithms predicted the diet quality indices with comparable accuracies (VFC, 0·69, 95 % CI 0·67, 0·71; OFC, 0·56, 95 % CI 0·54, 0·58; HEFI-2019, 0·66, 95 % CI 0·65, 0·67). ML algorithms trained to predict three diet quality indices in the general NutriQuébec sample were applicable to a high deprivation group.