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Fuzzy Logic-Driven Nutrition-Based Recommendation System for Gujarati Cardiac Patients: Integrating Cultural Preferences and Patient Feedback

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   Nirav Mehta,
Volume :  15 | Issue :  01 | Received :  March 13, 2024 | Accepted :  April 2, 2024 | Published :  April 18, 2024

[This article belongs to Journal of Computer Technology & Applications(jocta)]

Keywords

Nutrition-based recommendation system, Fuzzy logic, Gujarati cuisine, Cardiac patients, Dietary management, Cultural preferences, Personalized recommendations, Nutritive values, Dataset, Feedback integration

Abstract

This research focuses on developing a comprehensive dataset for accurate dietary recommendations tailored to Gujarati cardiac patients’ needs. The dataset comprises nutritional details of over 90 Gujarati food and fruit products, meticulously collected through primary and secondary data collection methods. Each food item’s nutritive values, including proteins, carbohydrates, fats, fiber, and calories, are meticulously recorded to facilitate precise dietary recommendations. The dataset integrates cultural preferences and seasonal variations in food availability to ensure relevance and adherence to dietary guidelines. Additionally, the research incorporates feedback from cardiac patients, who rate food preferences on a scale of 1 to 10, enhancing the dataset’s accuracy and relevance. By leveraging this rich dataset, the research aims to develop an effective recommendation system that provides personalized and culturally sensitive dietary guidance to improve the cardiac health management of Gujarati patients.

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References

  1. J. K. Kang, Z. Darby, T. P. Bleck, G. J. R. Whitman, B. S. Kim, and S.-M. Cho, “Post-Cardiac Arrest Care in Adult Patients After Extracorporeal Cardiopulmonary Resuscitation,” Crit Care Med, Mar. 2023, doi: 10.1097/ccm.0000000000006102.
  2. T. Aziz, N. Hussain, Z. Hameed, and L. Lin, “Elucidating the role of diet in maintaining gut health to reduce the risk of obesity, cardiovascular and other age-related inflammatory diseases: recent challenges and future recommendations,” Gut Microbes, vol. 16, no. 1. Taylor and Francis Ltd., 2024. doi: 10.1080/19490976.2023.2297864.
  3. A. Murayama, “Industry-sponsored meal payments are associated with prescriptions and Medicare expenditures on brand-name colchicine in the United States,” Int J Rheum Dis, vol. 27, no. 1, Jan. 2024, doi: 10.1111/1756-185X.14962.
  4. Dr. S. Manoharan and Prof. Sathish, “Patient Diet Recommendation System Using K Clique and Deep learning Classifiers,” Journal of Artificial Intelligence and Capsule Networks, vol. 2, no. 2, pp. 121–130, Jun. 2020, doi: 10.36548/jaicn.2020.2.005.
  5. C. Iwendi, S. Khan, J. H. Anajemba, A. K. Bashir, and F. Noor, “Realizing an Efficient IoMT-Assisted Patient Diet Recommendation System Through Machine Learning Model,” IEEE Access, vol. 8, pp. 28462–28474, 2020, doi: 10.1109/ACCESS.2020.2968537.
  6. F. Rehman, O. Khalid, N. U. Haq, A. U. R. Khan, K. Bilal, and S. A. Madani, “Diet-right: A smart food recommendation system,” KSII Transactions on Internet and Information Systems, vol. 11, no. 6, pp. 2910–2925, 2017, doi: 10.3837/tiis.2017.06.006.
  7. R. Z. Franco, “Online recommender system for personalized nutrition advice,” in RecSys 2017 – Proceedings of the 11th ACM Conference on Recommender Systems, Association for Computing Machinery, Inc, Aug. 2017, pp. 411–415. doi: 10.1145/3109859.3109862.
  8. L. Yang et al., “Yum-Me: A personalized nutrient-based meal recommender system,” ACM Trans Inf Syst, vol. 36, no. 1, Apr. 2017, doi: 10.1145/3072614.
  9. B. Janakiraman and S. Arumugam, “Personalized nutrition recommendation for diabetic patients using optimization techniques,” Intelligent Automation and Soft Computing, vol. 26, no. 2, pp. 269–280, 2020, doi: 10.31209/2019.100000150.
  10. A. Salam Khan and A. Hoffmann, “Building a case-based diet recommendation system without a knowledge engineer more widespread development and practical deployment of CBR systems in a large variety of application domains including many medical applications,” 2003.
  11. V. Espín, M. V. Hurtado, M. Noguera, and K. Benghazi, “Semantic-based recommendation of nutrition diets for the elderly from agroalimentary thesauri,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2013, pp. 471–482. doi: 10.1007/978-3-642-40769-7_41.
  12. B. Ali Al-Dhuhli, B. Samir Al-Gadidi, H. Hamed Al-Alawi, and K. Ali Al-Busaidi, “Developing a Nutrition and Diet Expert System Prototype.”
  13. T. Cioara et al., “Expert system for nutrition care process of older adults,” Future Generation Computer Systems, vol. 80, pp. 368–383, Mar. 2018, doi: 10.1016/j.future.2017.05.037.
  14. A. Starke, A. El Majjodi, and C. Trattner, “Boosting Health? Examining the Role of Nutrition Labels and Preference Elicitation Methods in Food Recommendation,” 2022. [Online]. Available: https://www.christophtrattner.info/
  15. H. Tejaswini, M. M. Manohara Pai, R. M. Pai, G. Attigeri, and R. P. Shenoy, “An ontology-based decision support system for nutrition deficiency,” in 2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2020 – Proceedings, Institute of Electrical and Electronics Engineers Inc., Oct. 2020, pp. 267–272. doi: 10.1109/DISCOVER50404.2020.9278069.
  16. G. Harshavardhini, K. B. Ramya, K. Madhuri, and M. Praveena, “A NOVEL INTEGRATED ML WITH NLP FRAMEWORK FOR DRUG RECOMMENDATION SYSTEM 1 Ms.CHIKKA HARSHINI.”
  17. A. Calero Valdez, M. Ziefle, K. Verbert, A. Felfernig, and A. Holzinger, “Recommender Systems for Health Informatics: State-of-the-Art and Future Perspectives”, doi: 10.1007/978-3-319.
  18. D. Tao, P. Yang, and H. Feng, “Utilization of text mining as a big data analysis tool for food science and nutrition,” Comprehensive Reviews in Food Science and Food Safety, vol. 19, no. 2. Blackwell Publishing Inc., pp. 875–894, Mar. 01, 2020. doi: 10.1111/1541-4337.12540.
  19. S. V Chavan and S. S. Sambare, “Study of Diet Recommendation System based on Fuzzy Logic and Ontology,” 2015.
  20. P. Dayang, C. S. Petsou, and D. W. Sambo, “Combining Fuzzy Logic and k-Nearest Neighbor Algorithm for Recommendation Systems,” International Journal of Information Technology and Computer Science, vol. 13, no. 4, pp. 1–16, Aug. 2021, doi: 10.5815/ijitcs.2021.04.01.
  21. H. A. Mohammed and H. Hagras, “Towards Developing Type 2 Fuzzy Logic Diet Recommendation System for Diabetes,” in 2018 10th Computer Science and Electronic Engineering Conference, CEEC 2018 – Proceedings, Institute of Electrical and Electronics Engineers Inc., Mar. 2019, pp. 56–59. doi: 10.1109/CEEC.2018.8674186.
  22. T. Osman, M. Mahjabeen, S. S. Psyche, A. I. Urmi, J. M. S. Ferdous, and R. M. Rahman, “Application of fuzzy logic for adaptive food recommendation,” International Journal of Fuzzy System Applications, vol. 6, no. 2, pp. 110–133, Apr. 2017, doi: 10.4018/IJFSA.2017040106.
  23. R. A. Priyono and K. Surendro, “Nutritional Needs Recommendation based on Fuzzy Logic,” Procedia Technology, vol. 11, pp. 1244–1251, 2013, doi: 10.1016/j.protcy.2013.12.320.
  24. M. A. Hussain et al., “Income Based Food List Recommendation for Rural People Using Fuzzy Logic,” in Proceedings – 17th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2018, Institute of Electrical and Electronics Engineers Inc., Sep. 2018, pp. 116–121. doi: 10.1109/ICIS.2018.8466403.
  25. A. Bilgin, H. Hagras, J. Van Helvert, and D. Alghazzawi, “A Linear General Type-2 Fuzzy-Logic-Based Computing with Words Approach for Realizing an Ambient Intelligent Platform for Cooking Recipe Recommendation,” IEEE Transactions on Fuzzy Systems, vol. 24, no. 2, pp. 306–329, Apr. 2016, doi: 10.1109/TFUZZ.2015.2453400.
  26. N. Tabassum, A. Rehman, M. Hamid, M. Saleem, S. Malik, and T. Alyas, “Intelligent nutrition diet recommender system for diabetic’s patients,” Intelligent Automation and Soft Computing, vol. 30, no. 1, pp. 319–335, 2021, doi: 10.32604/iasc.2021.018870.