AI Based Personalized Diet Planner for Patients

AI Based Personalized Diet Planner for Patients

Prof S. GOWTHAMI1., S. VARSHINI2, S.T. SUBHIKA3, N. SUVETHA4, S. PRIYADHARSHINI5

Assistant Professor,CSE,Vivekanandha college of technology for women1

Final year Student,CSE,Vivekanandha college of technology for women2,3,4,5

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Abstract - This project aims to develop an AI-powered system that provides personalized diet plans for patients based on their medical conditions, dietary restrictions, and nutritional needs. A goal of this research involves the development of an AI system that generates customized diet plans for patients depending on their medical health conditions and diet limitations and nutritional requirements. The system uses machine learning algorithms to evaluate patient information for creating dietary plans that follow their medical requirements and health objectives. This tool provides healthcare professionals together with dietician's valuable assistance to deliver individualized nutritional care to patients. The specific nutritional treatment requirements persist throughout the duration of the disease's diabetes, hypertension and cardiovascular conditions. Standard dietary approaches do not address unique health specifications and dietary requirements of individual persons. The system produces nutritional suggestions after it examines patients' medical records through evaluation of their dietary selection process together with tracking allergy risks and movement possibilities. The system implements continuous feedback processing to change diets through examination reports and biometric measurements. AI alongside OCR analysis reveals test results that then leads to summary findings and readjustments of nutritional approaches. Instant dietary help through chatbot construction provides quick support for patients to maintain increased use and better adherence to prescribed nutritional plans. Patients benefit from a platform that offers user-friendly personalized dietary plans which implement both medical advice and nutritional advice. Health professionals can better control chronic diseases through this solution by improving nutrition intake while both detecting necessary deficiencies and sending quick monitoring alerts. Through its conversion of nutrition planning into an interactive system the solution enhances patient outcomes.

   Over the years, there hasn't been much study on meal planner programmers based on compliance with macronutrient recommendations. We suggest developing a meal-planning programmed that can provide tailored diet plans depending on users' requirements.

Key Words:  AI diet planner, personalized nutrition, patient health data, machine learning, healthcare automation, random forest