Applicability of machine learning techniques in food intake assessment: A systematic review

Chaves, Larissa Oliveira; Domingos, Ana Luíza Gomes; Fernandes, Daniel L.; Cerqueira, Fábio Ribeiro; Siqueira-Batista, Rodrigo; Bressan, Josefina

doi:10.1080/10408398.2021.1956425

Cited by 17 publications

(3 citation statements)

References 56 publications

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“…However, the other models showed acceptable performance scores in predicting depression and anxiety. Thus, the findings of our study are consistent with other studies that assessed ML models in predicting depression and anxiety among adolescents and adults [4,6,17,[43][44][45][46][47][48]. In Priya et al [4], the NB model had the highest accuracy levels for anxiety, depression, and stress, whereas the F1-score showed that the RF model had the highest performance measure for stress symptoms.…”

Section: Principal Findings and Comparisons With Previous Worksupporting

confidence: 91%

“…In Priya et al [4], the NB model had the highest accuracy levels for anxiety, depression, and stress, whereas the F1-score showed that the RF model had the highest performance measure for stress symptoms. Furthermore, the results are consistent with other studies that assessed the ML models in predicting depression and anxiety among adults [44][45][46]. The studies showed that the ML models are efficient in predicting depression and anxiety symptoms.…”

Section: Principal Findings and Comparisons With Previous Worksupporting

confidence: 91%

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Assessment and Prediction of Depression and Anxiety Risk Factors in Schoolchildren: Machine Learning Techniques Performance Analysis

Qasrawi¹,

VicunaPolo²,

Al-Halawa³

et al. 2022

JMIR Form Res

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Background Depression and anxiety symptoms in early childhood have a major effect on children’s mental health growth and cognitive development. The effect of mental health problems on cognitive development has been studied by researchers for the last 2 decades. Objective In this paper, we sought to use machine learning techniques to predict the risk factors associated with schoolchildren’s depression and anxiety. Methods The study sample consisted of 3984 students in fifth to ninth grades, aged 10-15 years, studying at public and refugee schools in the West Bank. The data were collected using the health behaviors schoolchildren questionnaire in the 2013-2014 academic year and analyzed using machine learning to predict the risk factors associated with student mental health symptoms. We used 5 machine learning techniques (random forest [RF], neural network, decision tree, support vector machine [SVM], and naive Bayes) for prediction. Results The results indicated that the SVM and RF models had the highest accuracy levels for depression (SVM: 92.5%; RF: 76.4%) and anxiety (SVM: 92.4%; RF: 78.6%). Thus, the SVM and RF models had the best performance in classifying and predicting the students’ depression and anxiety. The results showed that school violence and bullying, home violence, academic performance, and family income were the most important factors affecting the depression and anxiety scales. Conclusions Overall, machine learning proved to be an efficient tool for identifying and predicting the associated factors that influence student depression and anxiety. The machine learning techniques seem to be a good model for predicting abnormal depression and anxiety symptoms among schoolchildren, so the deployment of machine learning within the school information systems might facilitate the development of health prevention and intervention programs that will enhance students’ mental health and cognitive development.

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Section: Principal Findings and Comparisons With Previous Worksupporting

confidence: 91%

Section: Principal Findings and Comparisons With Previous Worksupporting

confidence: 91%

Assessment and Prediction of Depression and Anxiety Risk Factors in Schoolchildren: Machine Learning Techniques Performance Analysis

Qasrawi¹,

VicunaPolo²,

Al-Halawa³

et al. 2022

JMIR Form Res

View full text Add to dashboard Cite

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“…Each stage from food processing to consuming in this system can be replaced with data-driven computational methods to prompt the development of food science and industry, such as the use of neural networks in modeling the food process, 2 , 3 food quality assessment, 4 food object recognition and analysis, 5 , 6 , 7 food authentication and traceability, 8 and dietary assessment. 9 , 10 …”

Section: Introductionmentioning

confidence: 99%

Applications of knowledge graphs for food science and industry

Min

et al. 2022

Patterns

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Artificial intelligence in clinical nutrition and dietetics: A brief overview of current evidence

Atwal

2024

Nut in Clin Prac

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The rapid surge in artificial intelligence (AI) has dominated technological innovation in today's society. As experts begin to understand the potential, a spectrum of opportunities could yield a remarkable revolution. The upsurge in healthcare could transform clinical interventions and outcomes, but it risks dehumanization and increased unethical practices. The field of clinical nutrition and dietetics is no exception. This article finds a multitude of developments underway, which include the use of AI for malnutrition screening; predicting clinical outcomes, such as disease onset, and clinical risks, such as drug interactions; aiding interventions, such as estimating nutrient intake; applying precision nutrition, such as measuring postprandial glycemic response; and supporting workflow through chatbots trained on natural language models. Although the opportunity and scalability of AI is incalculably attractive, especially in the face of poor healthcare resources, the threat cannot be ignored. The risk of malpractice and lack of accountability are some of the main concerns. As such, the healthcare professional's responsibility remains paramount. The data used to train AI models could be biased, which could risk the quality of care to vulnerable or minority patient groups. Standardized AI‐development protocols, benchmarked to care recommendations, with rigorous large‐scale validation are required to maximize application among different settings. AI could overturn the healthcare landscape, and this article skims the surface of its potential in clinical nutrition and dietetics.

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Applicability of machine learning techniques in food intake assessment: A systematic review

Cited by 17 publications

References 56 publications

Assessment and Prediction of Depression and Anxiety Risk Factors in Schoolchildren: Machine Learning Techniques Performance Analysis

Assessment and Prediction of Depression and Anxiety Risk Factors in Schoolchildren: Machine Learning Techniques Performance Analysis

Applications of knowledge graphs for food science and industry

Artificial intelligence in clinical nutrition and dietetics: A brief overview of current evidence

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