A Hybrid Recommender System for Personalized Dietary Management in Phenylketonuria (PKU)
Mardhiat Ajetunmobi
Co-Presenters: Individual Presentation
College: The Dorothy and George Hennings College of Science, Mathematics and Technology
Major: Biotechnology/Molecular Biology - STEM 5 Year B.S./M.S.
Faculty Research Mentor: Malihe Aliasgari
Abstract:
Phenylketonuria (PKU) is a rare metabolic disorder that requires strict dietary management to prevent neurological damage. Adhering to the dietary restrictions is crucial for PKU patients, as they must limit their intake of phenylalanine while ensuring they meet essential nutritional needs. This study presents a hybrid recommender system designed to assist PKU patients in making personalized dietary choices. The system integrates collaborative filtering and content-based filtering to provide accurate and relevant food recommendations tailored to each patient’s unique preferences and dietary restrictions. Moreover, the recommender system incorporates time-aware techniques, considering the temporal aspects of dietary planning. By incorporating both nutritional guidelines and individual preferences, the system delivers personalized, time-sensitive recommendations that help optimize the PKU diet. The hybrid model enhances the accuracy of food suggestions, improving the adherence to PKU dietary restrictions and promoting better health outcomes. Ultimately, this system aims to support PKU patients in managing their condition more effectively, offering an innovative, data-driven tool for dietary planning that adjusts to both individual and time-dependent needs.