Nutritional biomarkers and machine learning for personalized nutrition applications and health optimization

Panagoulias, Dimitrios P.; Sotiropoulos, Dionisios N.; Tsihrintzis, George A.

doi:10.3233/idt-210233

Cited by 11 publications

(9 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this paper, we report on recent findings from our research on investigating the link between a person's standard biochemistry profile (based on blood exams), his/her body mass index (BMI), metabolism as health state and SBP. Our current findings expand upon our previous related research, which was based on the use of deep neural networks and other machine learning paradigms in relation to BMI and nutrition [3][4][5][6].…”

Section: Introductionsupporting

confidence: 80%

See 1 more Smart Citation

SVM-Based Blood Exam Classification for Predicting Defining Factors in Metabolic Syndrome Diagnosis

2022

Self Cite

View full text Add to dashboard Cite

Biomarkers have already been proposed as powerful classification features for use in the training of neural network-based and other machine learning and artificial intelligence-based prognostic models in the scientific field of personalized nutrition. In this paper, we construct and study cascaded SVM-based classifiers for automated metabolic syndrome diagnosis. Specifically, using blood exams, we achieve an average accuracy of about 84% in correctly classifying body mass index. Similarly, cascaded SVM-based classifiers achieve a 74% accuracy in correctly classifying systolic blood pressure. Next, we propose and implement a system that achieves an 84% accuracy in metabolic syndrome prediction. The proposed system relies not only on prediction of the body mass index but also on prediction from blood exams of total cholesterol, triglycerides and glucose. For the aim of self-completeness of the paper, the key concepts with regard to metabolic syndrome are summarized, and a review of previous related work is included. Finally, conclusions are drawn and indications for related future research are outlined.

show abstract

Section: Introductionsupporting

confidence: 80%

“…We show that support vector machinebased classifiers are very promising. We also provide a brief comparison with results on previous works of ours that were based on deep neural networks [3][4][5][6]. Moreover, a more extensive look into related literature is provided in Section 3.…”

Section: Introductionmentioning

confidence: 98%

SVM-Based Blood Exam Classification for Predicting Defining Factors in Metabolic Syndrome Diagnosis

2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…A total of three (9.7%) of the identified papers were assigned to the cluster "Disease Diagnosis and Monitoring". Panagoulias et al [50] focus on applying machine learning to personalized nutrition and health optimization biomarkers in this context. The authors present the use of metabolomics to study unique chemical fingerprints left by cellular processes and the potential of neural networks to evaluate nutritional biomarkers, predict body mass index (BMI), and discover dietary patterns.…”

Section: Disease Diagnosis and Monitoringmentioning

confidence: 99%

“…Food recognition is more effective when it follows dietary assessment because it facilitates the user's decision on food choices and considers user expectations and requirements after the evaluation. Furthermore, patients with specific medical conditions can use AI techniques for disease-predictive modeling, diagnosis, and monitoring [42][43][44][45][46][47][48][49][50][51][52]. The application of AI aims to prevent and control disease development in the human body from a nutritional perspective.…”

Section: Conceptual Framework For Applying Ai ML and Dl In Nutritionmentioning

confidence: 99%

Applications of Artificial Intelligence, Machine Learning, and Deep Learning in Nutrition: A Systematic Review

Theodore Armand,

Nfor,

Kim

et al. 2024

Nutrients

View full text Add to dashboard Cite

In industry 4.0, where the automation and digitalization of entities and processes are fundamental, artificial intelligence (AI) is increasingly becoming a pivotal tool offering innovative solutions in various domains. In this context, nutrition, a critical aspect of public health, is no exception to the fields influenced by the integration of AI technology. This study aims to comprehensively investigate the current landscape of AI in nutrition, providing a deep understanding of the potential of AI, machine learning (ML), and deep learning (DL) in nutrition sciences and highlighting eventual challenges and futuristic directions. A hybrid approach from the systematic literature review (SLR) guidelines and the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines was adopted to systematically analyze the scientific literature from a search of major databases on artificial intelligence in nutrition sciences. A rigorous study selection was conducted using the most appropriate eligibility criteria, followed by a methodological quality assessment ensuring the robustness of the included studies. This review identifies several AI applications in nutrition, spanning smart and personalized nutrition, dietary assessment, food recognition and tracking, predictive modeling for disease prevention, and disease diagnosis and monitoring. The selected studies demonstrated the versatility of machine learning and deep learning techniques in handling complex relationships within nutritional datasets. This study provides a comprehensive overview of the current state of AI applications in nutrition sciences and identifies challenges and opportunities. With the rapid advancement in AI, its integration into nutrition holds significant promise to enhance individual nutritional outcomes and optimize dietary recommendations. Researchers, policymakers, and healthcare professionals can utilize this research to design future projects and support evidence-based decision-making in AI for nutrition and dietary guidance.

show abstract

“…This is a form of reasoning that deals with uncertain or ambiguous information, where the conclusions or decisions reached may not be completely accurate or certain. AI-based reasoning encompasses various techniques like machine learning [15][16][17][18][19], deep learning [20,21],…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence and User Experience in reciprocity: Contributions and state of the art

Virvou¹

2023

IDT

View full text Add to dashboard Cite

Among the primary aims of Artificial Intelligence (AI) is the enhancement of User Experience (UX) by providing deep understanding, profound empathy, tailored assistance, useful recommendations, and natural communication with human interactants while they are achieving their goals through computer use. To this end, AI is used in varying techniques to automate sophisticated functions in UX and thereby changing what UX is apprehended by the users. This is achieved through the development of intelligent interactive systems such as virtual assistants, recommender systems, and intelligent tutoring systems. The changes are well received, as technological achievements but create new challenges of trust, explainability and usability to humans, which in turn need to be amended by further advancements of AI in reciprocity. AI can be utilised to enhance the UX of a system while the quality of the UX can influence the effectiveness of AI. The state of the art in AI for UX is constantly evolving, with a growing focus on designing transparent, explainable, and fair AI systems that prioritise user control and autonomy, protect user data privacy and security, and promote diversity and inclusivity in the design process. Staying up to date with the latest advancements and best practices in this field is crucial. This paper conducts a critical analysis of published academic works and research studies related to AI and UX, exploring their interrelationship and the cause-effect cycle between the two. Ultimately, best practices for achieving a successful interrelationship of AI in UX are identified and listed based on established methods or techniques that have been proven to be effective in previous research reviewed.

show abstract

Nutritional biomarkers and machine learning for personalized nutrition applications and health optimization

Cited by 11 publications

References 26 publications

SVM-Based Blood Exam Classification for Predicting Defining Factors in Metabolic Syndrome Diagnosis

SVM-Based Blood Exam Classification for Predicting Defining Factors in Metabolic Syndrome Diagnosis

Applications of Artificial Intelligence, Machine Learning, and Deep Learning in Nutrition: A Systematic Review

Artificial Intelligence and User Experience in reciprocity: Contributions and state of the art

Contact Info

Product

Resources

About