LEVERAGING MACHINE LEARNING ALGORITHMS FOR NUTRITIONAL DEFICIENCY PREDICTION FROM DIETARY PATTERNS

Abstract

Abstract: Nutritional deficiencies, particularly in essential vitamins and minerals, pose significant health risks globally. Early detection of such deficiencies is crucial for preventing chronic diseases and improving overall well-being. Traditional methods of assessing nutritional status, such as blood tests and clinical evaluations, are invasive, time-consuming, and often inaccessible to many populations. With the growing availability of dietary data, machine learning (ML) algorithms offer a promising alternative for predicting nutritional deficiencies based on dietary patterns. This paper explores the application of various machine learning algorithms to predict nutritional deficiencies by analyzing dietary intake data. The study evaluates the effectiveness of different ML models, including decision trees, support vector machines, and neural networks, in identifying deficiencies of key nutrients such as iron, vitamin D, and calcium. The research employs a dataset comprising dietary records from diverse population groups, annotated with nutritional outcomes verified through clinical assessments. Feature engineering techniques are applied to extract meaningful patterns from the dietary data, followed by model training and validation. The models are evaluated based on their accuracy, precision, recall, and F1-score, with a focus on the trade-offs between model complexity and interpretability. The results demonstrate that machine learning algorithms can achieve high accuracy in predicting nutritional deficiencies, with some models outperforming traditional statistical methods. Decision trees, for instance, provided interpretable models with competitive accuracy, while neural networks offered higher accuracy at the cost of reduced interpretability. The study also highlights the importance of selecting appropriate features and the potential for incorporating additional data sources, such as lifestyle factors and genetic information, to enhance prediction accuracy. The findings suggest that machine learning models can be integrated into health monitoring systems to provide non-invasive, cost-effective, and scalable solutions for nutritional deficiency screening. Such systems could empower individuals and healthcare providers with timely insights into nutritional status, enabling early interventions and personalized dietary recommendations.