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Personalized Nutrition Recommendation for Diabetic Patients Using Optimization Techniques

Bhavithra Janakiraman1,*, Saradha Arumugam2

1 Department of Computer Science and Engineering, Dr. Mahalingam College of Engineering and Technology, Pollachi, Tamilnadu 642003, India
2 Department of Computer Science and Engineering, Institute of Road and Transport Technology, Erode, Tamilnadu 638316, India

* Corresponding Author: Bhavithra Janakiraman, email

Intelligent Automation & Soft Computing 2020, 26(2), 269-280.


Personalization in recommendation system has been emerging as the most predominant area in service computing. Collaborative filtering and content based approaches are two major techniques applied for recommendation. However, to improve the accuracy and enhance user satisfaction, optimization techniques such as Ant Colony and Particle Swarm Optimization were analyzed in this paper. For theoretical analysis, this paper investigates web page recommender system. For experimentation, Diabetic patient’s health records were investigated and recommendation algorithms are applied to suggest appropriate nutrition for improving their health. Experiment result shows that Particle Swarm Optimization outperforms other traditional methods with improved performance and accuracy.


Cite This Article

B. Janakiraman and S. Arumugam, "Personalized nutrition recommendation for diabetic patients using optimization techniques," Intelligent Automation & Soft Computing, vol. 26, no.2, pp. 269–280, 2020.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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