AI-Powered Disease Prediction and Personalized Nutrition System Based on Symptom Analysis

In the digital era, disease diagnosis and patient management is taking a decisive leap forward to dynamically link personalized medical decision making, disease recognition and management with the massive individuality and uniqueness of the human body. Personalized nutrition utilizes user-specific data, including genetic, metabolic and lifestyle factors to optimize dietary recommendations. Traditional dietary guidelines are often generic and fail to consider individual differences, leading to suboptimal health outcomes. This study examines the role of artificial intelligence (AI), machine learning and real-time monitoring in personalized nutrition. By integrating user data, AI-driven models can enhance adherence and prevent diet-related chronic diseases. The paper also explores the challenges in implementing personalized nutrition at scale such as data privacy, accuracy and integration with healthcare systems.