Analysis of meal patterns with the use of supervised data mining techniques—artificial neural networks and decision trees

Hearty, Á. P.; Gibney, Michael J.

doi:10.3945/ajcn.2008.26619

Cited by 65 publications

(62 citation statements)

References 25 publications

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“…Other data mining techniques, such as neural network approaches might also be promising to obtain insight into the multiple dietary factors or a combination of diet and other risk factors that predict a disease outcome. Only a few applications including dietary variables have been published (9,(54)(55)(56)(57) .…”

Section: Principlesmentioning

confidence: 99%

“…The structure of the classification tree model is a set of nodes from the top to the bottom, in which the terminal nodes show the specific pattern features of the subpopulations in percentage, including the number of people and the probability or mean values of the outcome variable (53) . Until now, decision tree analysis was seldom applied for dietary pattern analysis (9) or in a broader risk factor pattern analysis including dietary and other variables (49) . Other data mining techniques, such as neural network approaches might also be promising to obtain insight into the multiple dietary factors or a combination of diet and other risk factors that predict a disease outcome.…”

Section: Principlesmentioning

confidence: 99%

“…The predictor variables can be biomarkers that are intermediate risk factors for a dietary-related disease (48) , but also other risk factors can be used such as nutrients that are related to the outcome of interest (8) , a disease itself (49) or an overall dietary quality score based on recommendations for a healthy diet (9) . The most commonly used hybrid approach in the field of dietary pattern analysis is reduced rank regression (e.g.…”

Section: Principlesmentioning

confidence: 99%

“…For example, reduced rank regression can be applied to define linear combinations of food intakes that maximally explain predictors of disease (8) . Methods such as decision tree analysis can be used to identify subgroups of a population whose members share dietary characteristics that influence a disease or disease predictor (9) . This paper aims to present an overview of approaches for assessing the overall diet.…”

Section: Proceedings Of the Nutrition Societymentioning

confidence: 99%

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Evaluation of methodologies for assessing the overall diet: dietary quality scores and dietary pattern analysis

2013

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This paper aims to describe different approaches for studying the overall diet with advantages and limitations. Studies of the overall diet have emerged because the relationship between dietary intake and health is very complex with all kinds of interactions. These cannot be captured well by studying single dietary components. Three main approaches to study the overall diet can be distinguished. The first method is researcher-defined scores or indices of diet quality. These are usually based on guidelines for a healthy diet or on diets known to be healthy. The second approach, using principal component or cluster analysis, is driven by the underlying dietary data. In principal component analysis, scales are derived based on the underlying relationships between food groups, whereas in cluster analysis, subgroups of the population are created with people that cluster together based on their dietary intake. A third approach includes methods that are driven by a combination of biological pathways and the underlying dietary data. Reduced rank regression defines linear combinations of food intakes that maximally explain nutrient intakes or intermediate markers of disease. Decision tree analysis identifies subgroups of a population whose members share dietary characteristics that influence (intermediate markers of) disease. It is concluded that all approaches have advantages and limitations and essentially answer different questions. The third approach is still more in an exploration phase, but seems to have great potential with complementary value. More insight into the utility of conducting studies on the overall diet can be gained if more attention is given to methodological issues.

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Section: Principlesmentioning

confidence: 99%

Section: Principlesmentioning

confidence: 99%

Section: Principlesmentioning

confidence: 99%

Section: Proceedings Of the Nutrition Societymentioning

confidence: 99%

See 2 more Smart Citations

Evaluation of methodologies for assessing the overall diet: dietary quality scores and dietary pattern analysis

2013

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“…This leads to the possibility of grouping individuals according to their metabolic profiles, an approach that is known as metabotyping. Complex datasets can be systematically studied using a wide variety of data-mining techniques (21) . In nutrition, the most widely used methods involve cluster analysis and this technique has been extensively used to study food intake patterns (22) .…”

Section: Personalised Nutrition Based On Personalised Phenotypic Datamentioning

confidence: 99%

The future direction of personalised nutrition: my diet, my phenotype, my genes

Gibney

Walsh

2013

Proc. Nutr. Soc.

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Although personalised nutrition is frequently considered in the context of diet-gene interactions, increasingly, personalised nutrition is seen to exist at three levels. The first is personalised dietary advice using Internet-delivered services, which ultimately will become automated and which will also draw on mobile phone technology. The second level of personalised dietary advice will include phenotypic information on anthropometry, physical activity, clinical parameters and biochemical markers of nutritional status. It remains possible that in addition to personalised dietary advice based on phenotypic data, advice at that group or metabotype level may be offered where metabotypes are defined by a common metabolic profile. The third level of personalised nutrition will involve the use of genomic data. While the genomic aspect of personalised nutrition is often considered as its main driver, there are significant challenges to translation of data on SNP and diet into personalised advice. The majority of the published data on SNP and diet emanate from observational studies and as such do not offer any cause-effect associations. To achieve this, purpose-designed dietary intervention studies will be needed with subjects recruited according to their genotype.

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AI Advancements in Food, Nutrition and Dietetics: A Comprehensive Survey

Kulkarni,

Lawate,

Jaiswal

et al. 2024

Lecture Notes in Networks and Systems

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Analysis of meal patterns with the use of supervised data mining techniques—artificial neural networks and decision trees

Abstract: ANNs and decision trees were successfully used to predict an aspect of dietary quality. However, further exploration of the use of ANNs and decision trees in dietary pattern analysis is warranted.

Cited by 65 publications

References 25 publications

Evaluation of methodologies for assessing the overall diet: dietary quality scores and dietary pattern analysis

Evaluation of methodologies for assessing the overall diet: dietary quality scores and dietary pattern analysis

The future direction of personalised nutrition: my diet, my phenotype, my genes

AI Advancements in Food, Nutrition and Dietetics: A Comprehensive Survey

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