Precision Nutrition: Leveraging Machine Learning for Personalized Dietary Recommendations and Health Outcomes
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Predictive nutrition is a relatively young science that aims at guiding the consumers to adhere to those diets that would match their specific genotype, gender, behavior patterns, and health conditions. a subfield of AI known as machine learning (ML) has revolutionized practice in this realm due to providing approaches for considering massive amounts of data and using data-driven interventions for individual health enhancement. Precision nutrition is discussed in this paper to include the possibility of using ML techniques in the process in order to enhance health outcomes of patients. Through incorporation of various datasets such as genetic profile, biomarkers data and food consumption data, it is possible for ML models to predict and develop diets forecast with greater precision than in the past. Ethical issues, variability, and algorithm bias are also discussed with recommendations on ways to make the model more reliable and usable for all stakeholders. This work establishes the potential of ML in improving precision nutrition and specifies the need for interdisciplinarity to advance innovative, human-focused digital dietary solutions.
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