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A Nutritional Recommendation Expert System for Ailing Human

Author

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  • Ekwealor, Oluchukwu Uzoamaka

    (Department of Computer Science, Nnamdi Azikiwe University, Awka, Nigeria)

  • Ezuruka, Evelyn Ogochukwu

    (Department of Computer Science, Nnamdi Azikiwe University, Awka, Nigeria)

  • Uchefuna Charles Ikenna

    (Department of Computer Science, Federal Polytechnic, Oko, Nigeria)

  • Chukwudum, Chiemeka Prince

    (Department of Forensic Science, Nnamdi Azikiwe University, Awka, Nigeria)

Abstract

A nutritional recommendation expert system for ailing human is an expert system that diagnoses, controls and monitors human nutrition. Its aim is to assess human nutritional health in order to provide medical advice and recommend healthy nutritional habits for the users. The system has a user-friendly interface through which the users can provide their personal health information. Based on this information, the system assesses their health status and gives them medical guidelines as well as recommends appropriate dietary habit to promote their healthy living. The methodology adopted for this work was the structured systems analysis and design methodology and the system was developed using MySQL, CSS and HTML. This system is very helpful to health clinics, fitness centers, educational institutions and dieticians.

Suggested Citation

  • Ekwealor, Oluchukwu Uzoamaka & Ezuruka, Evelyn Ogochukwu & Uchefuna Charles Ikenna & Chukwudum, Chiemeka Prince, 2025. "A Nutritional Recommendation Expert System for Ailing Human," International Journal of Research and Scientific Innovation, International Journal of Research and Scientific Innovation (IJRSI), vol. 12(15), pages 735-740, April.
    • Handle: RePEc:bjc:journl:v:12:y:2025:i:15:p:735-740
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    References listed on IDEAS

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    1. Vanderlei Carneiro Silva & Bartira Gorgulho & Dirce Maria Marchioni & Sheila Maria Alvim & Luana Giatti & Tânia Aparecida de Araujo & Angelica Castilho Alonso & Itamar de Souza Santos & Paulo Andrade , 2022. "Recommender System Based on Collaborative Filtering for Personalized Dietary Advice: A Cross-Sectional Analysis of the ELSA-Brasil Study," IJERPH, MDPI, vol. 19(22), pages 1-12, November.
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