Personalized Medical Diet Recommendations for Disease Management and Improved Patient Outcomes

Abstract

Personalized health diets play a crucial role in infection management by tailoring diet recommendation systems to routine data, genetic factors, and specific medical conditions. Research introduces the Intelligent Nutcracker Optimized Effective Decision Tree (INO-EDT) model, designed to provide individualized nutritional guidance for managing chronic illnesses, particularly diabetes and heart disease. Medical files, questionnaires, wearable devices, and food journals serve as sources of patient data standardization and cleaning to ensure accuracy and stability. Machine Learning (ML) techniques analyze individual patient profiles to develop personalized nutrition plans that are effective, sustainable, and adaptable. The INO-EDT model incorporates a nutcracker-inspired optimization technique to enhance decision tree accuracy, fine-tuning diet recommendations based on patient-specific factors. This optimization ensures proper diet interventions with enhanced efficacy of dietary interventions in disease organization. The outcome confirms that the INO-EDT model was more accurate (98.40%), demonstrating its ability to generate proper, data-backed dietary advice. By optimizing personalized nutritional interventions, the INO-EDT model enables healthcare providers to offer more effective, patient-centered solutions, reducing complications connected with chronic diseases. This approach enhances patient outcomes by integrating intellectual algorithms that consider multiple health parameters to create a customized diet strategy. The results highlight the potential of AI-driven dietary recommendation systems in enhancing disease management, improving adherence to medical diet systems, and elevating overall quality of life. Future research will aim to expand the model's capabilities by integrating additional health markers for broader clinical applications.