Abstract

Cardiovascular diseases (CVDs) remain a significant health concern globally, with a substantial burden on populations worldwide. In regions like Gujarat, India, where cultural dietary preferences Play a pivotal role in daily food choices, effective management of CVDs requires personalized dietary
recommendations tailored to the local context. This paper presents a novel approach to addressing this challenge by developing a fuzzy logic-driven nutrition-based recommendation system (NBRS) for
Gujarati cardiac patients.

Keyworde: Nutrition-based recommendation system, fuzzy logic, Gujarati cuisine, cardiac patients, dietary management, cultural preferences, personalised recommendations, nutritive values, dataset, feedback integration

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Refrences:

1. Kang JK, Darby Z, Bleck TP, Whitman GJR, Kim BS, Cho S-M. Post-cardiac arrest care in adult
patients after extracorporeal cardiopulmonary resuscitation. Crit Care Med. 2023; 52 (3): 483–494.
doi: 10.1097/ccm.0000000000006102.
2. Aziz T, Hussain N, Hameed Z, Lin L. 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. 2024; 16 (1): 2297864. doi:
10.1080/19490976.2023.2297864.
3. Murayama A. Industry-sponsored meal payments are associated with prescriptions and Medicare
expenditures on brand-name colchicine in the United States. Int J Rheum Dis. 2024; 27 (1): e14962.
doi: 10.1111/1756-185X.14962.
4. Manoharan S, Amaippan. S. Patient diet recommendation system using K Clique and deep learning
classifiers. J Artif Intell Capsule Netw. 2020; 2 (2): 121–130. doi: 10.36548/jaicn.2020.2.005.
5. Iwendi C, Khan S, Anajemba JH, Bashir AK, Noor F. Realizing an efficient IoMT-assisted patient
diet recommendation system through machine learning model. IEEE Access. 2020; 8: 28462–
28474. doi: 10.1109/ACCESS.2020.2968537.
6. Rehman F, Khalid O, Haq NU, Khan AUR, Bilal K, Madani SA. Diet-right: a smart food
recommendation system. KSII Trans Internet Inform Syst. 2017; 11 (6): 2910–2925. doi:
10.3837/tiis.2017.06.006.
7. Franco RZ. Online recommender system for personalized nutrition advice. In: RecSys 2017 –
Proceedings of the 11th ACM Conference on Recommender Systems, Como, Italy, August 27–31,
2017. pp. 411–415. doi: 10.1145/3109859.3109862.
8. Yang L, Hsieh CK, Yang H, Pollak JP, Dell N, Belongie S, Cole C, Estrin D. Yum-Me: a
personalized nutrient-based meal recommender system. ACM Trans Inform Syst. 2017; 36 (1): 7.
doi: 10.1145/3072614.
9. Janakiraman B, Arumugam S. Personalized nutrition recommendation for diabetic patients using
optimization techniques. Intell Automat Soft Comput. 2020; 26 (2): 269–280. doi:
10.31209/2019.100000150.
10. Khan AS, Hoffmann A. 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. Artif Intell Med. 2003; 27 (2): 155–179.
11. Espín V, Hurtado MV, Noguera M, Benghazi K. Semantic-based recommendation of nutrition diets
for the elderly from agroalimentary thesauri. In: Larsen HL, Martin-Bautista MJ, Vila MA,
Andreasen T, Christiansen H, editors. Flexible Query Answering Systems. FQAS 2013. Lecture
Notes in Computer Science Volume 8132 (including subseries Lecture Notes in Artificial
Intelligence and Lecture Notes in Bioinformatics). Heidelberg, Germany: Springer; 2013. pp. 471–
482. doi: 10.1007/978-3-642-40769-7_41.
12. Al-Dhuhli BA, Al-Gadidi BS, Al-Alawi HH, Al-Busaidi KA. Developing a nutrition and diet expert
system prototype. In: 21 International Business Information Management Association Conference,
Vienna, Austria, June 27–28, 2013.. pp. 1368–1375.
13. Cioara T, Anghel I, Salomie I, Barakat L, Miles S, Reidlinger D, Taweel A, Dobre C, Pop F. Expert
system for nutrition care process of older adults. Future Gener Computer Syst. 2018; 80: 368–383.
doi: 10.1016/j.future.2017.05.037.
14. Starke A, El Majjodi A, Trattner C. Boosting health? Examining the role of nutrition labels and
preference elicitation methods in food recommendation. In: IntRS’22: Joint Workshop on Interfaces
and Human Decision Making for Recommender Systems, Seattle, WA, UA, September 22, 2022.
Available at https://ceur-ws.org/Vol-3222/paper5.pdf
15. Tejaswini H, Pai MMM, Pai RM, Attigeri G, Shenoy RP. An ontology-based decision support
system for nutrition deficiency. In: 2020 IEEE International Conference on Distributed Computing,
VLSI, Electrical Circuits and Robotics, DISCOVER 2020, Udupi, India, October 30–31, 2020.pp.
267–272. doi: 10.1109/DISCOVER50404.2020.9278069.
16. Harshini C, Harshavardhini G, Ramya KB, Madhuri K, Praveena M. A novel integrated ML with
NLP framework for drug recommendation system. J Eng Sci. 2023; 14 (10): 38–44.
17. Valdez AC, Ziefle M, Verbert K, Felfernig A, Holzinger A. Recommender systems for health
informatics: state-of-the-art and future perspectives. In: Holzinger A, editor. Machine Learning for
Health Informatics. Cham, Switzerland: Springer; 2016. pp. 391–414. doi: 10.1007/978-3-319.
18. Tao D, Yang P, Feng H. Utilization of text mining as a big data analysis tool for food science and
nutrition. Comprehens Rev Food Sci Food Saf. 2020; 19 (2): 875–894. doi: 10.1111/1541-
4337.12540.
19. Chavan SV, Sambare SS. Study of diet recommendation system based on fuzzy logic and ontology.
Int J Computer Appl. 2015; 132 (12): 20–24.
20. Dayang P, Petsou CS, Sambo DW. Combining fuzzy logic and k-nearest neighbor algorithm for
recommendation systems. Int J Inform Technol Computer Sci. 2021; 13 (4): 1–16. doi:
10.5815/ijitcs.2021.04.01.
21. Mohammed HA, Hagras H. Towards developing type 2 fuzzy logic diet recommendation system
for diabetes. In: 2018 10th Computer Science and Electronic Engineering Conference, CEEC,
Colchester, UK, September 19–21, 2018. pp. 56–59. doi: 10.1109/CEEC.2018.8674186.
22. Osman T, Mahjabeen M, Psyche SS, Urmi AI, Ferdous JMS, Rahman RM. Application of fuzzy
logic for adaptive food recommendation. Int J Fuzzy Syst Appl. 2017; 6 (2): 110–133. doi:
10.4018/IJFSA.2017040106.
23. Priyono RA, Surendro K. Nutritional needs recommendation based on fuzzy logic. Procedia
Technol. 2013; 11: 1244–1251. doi: 10.1016/j.protcy.2013.12.320.
24. Hussain MA, Yeasmin S, Chowdhury S, Wasee FR, Afrin S, Tanzim SM, Rahman RM. Income
based food list recommendation for rural people using fuzzy logic. In: 17th IEEE/ACIS
International Conference on Computer and Information Science, ICIS, Singapore, June 6–8, 2018.
pp. 116–121. doi: 10.1109/ICIS.2018.8466403.
25. Bilgin A, Hagras H, Van Helvert J, Alghazzawi D. A linear general type-2 fuzzy-logic-based
computing with words approach for realizing an ambient intelligent platform for cooking recipe
recommendation. IEEE Trans Fuzzy Syst. 2016; 24 (2): 306–329. doi:
10.1109/TFUZZ.2015.2453400.
26. Tabassum N, Rehman A, Hamid M, Saleem M, Malik S, Alyas T. Intelligent nutrition diet
recommender system for diabetic patients. Intell Automat Soft Comput. 2021; 30 (1): 319–335. doi:
10.32604/iasc.2021.018870.