A comparison of statistical and machine-learning techniques in evaluating the association between dietary patterns and 10-year cardiometabolic risk (2002-2012): the ATTICA study.

Statistical methods are usually applied in examining diet-disease associations, whereas factor analysis is commonly used for dietary pattern recognition. Recently, machine learning (ML) has been also proposed as an alternative technique in health classification. In this work, the predictive accuracy of statistical v. ML methodologies as regards the association of dietary patterns on CVD risk was tested. During 2001-2002, 3042 men and women (45 (sd 14) years) were enrolled in the ATTICA study. In 2011-2012, the 10-year CVD follow-up was performed among 2020 participants. Item Response Theory was applied to create a metric of combined 10-year cardiometabolic risk, the 'Cardiometabolic Health Score', that incorporated incidence of CVD, diabetes, hypertension and hypercholesterolaemia. Factor analysis was performed to extract dietary patterns, on the basis of either foods or nutrients consumed; linear regression analysis was used to assess their association with the cardiometabolic score. Two ML techniques (k-nearest-neighbor's algorithm and random-forests decision tree) were applied to evaluate participants' health based on dietary information. Factor analysis revealed five and three factors from foods and nutrients, respectively, explaining 54 and 65 % of the total variation in intake. Nutrient and food pattern regression models showed similar accuracy in correctly classifying an individual according to the cardiometabolic risk (R 2=9·6 % and R 2=8·3 %, respectively). ML techniques were superior compared with linear regression in correct classification of the individuals according to the Health Score (accuracy approximately 38 v. 6 %, respectively), whereas the two ML methods showed equal classification ability. Conclusively, ML methods could be a valuable tool in the field of nutritional epidemiology, leading to more accurate disease-risk evaluation.

Repeatability of dietary patterns extracted through multivariate statistical methods: a literature review in methodological issues.

In the last few years, the need for processing large amount of data in nutrition science was dramatically arose. This created the need to apply, primarily, advanced analytical research methods that could enable researchers to handle the large amount of information. Dietary pattern analysis is a commonly used approach to enable and incorporate this phenomenon in nutrition research. This article reviews the most common dietary pattern's assessment statistical methods, evaluating at the same time the up-to-day knowledge regarding the reliability and validity of the retrieved patterns. The review is based on both a-priori (diet scores) and a-posteriori (multivariate statistical analysis) methods. The reports from the existing few studies suggest that the use of both a-priori and a-posteriori pattern analyses in nutrition surveys should be made with consciousness. The suggestion of new statistical techniques for the control of repeatability of dietary patterns is considered essential.