ISSN : 2583-2646

Health-Care Recommender System Using Collaborative Filtering Algorithm

ESP Journal of Engineering & Technology Advancements
© 2023 by ESP JETA
Volume 3  Issue 2
Year of Publication : 2023
Authors : Ubani Chinyere, Mark Uvietesiri, Syvester Akhetuamen, Morgan Obi


Ubani Chinyere, Mark Uvietesiri, Syvester Akhetuamen, Morgan Obi, 2023. "Health-Care Recommender System Using Collaborative Filtering Algorithm" ESP Journal of Engineering & Technology Advancements  3(2): 59-64.


Presently, there are thousands of hospitals offering several types of services to patients. It becomes challenging for a patient to make an informed decision on which hospital to visit for treatment for a particular ailment. Recommender systems have been used in diverse areas to solve the problem of decision making by providing several options for users based on certain attributes of the user that are similar to that of other users with similar attributes. In this work, we design a system that will recommend hospital for sick patient using collaborative filtering algorithm. It is aimed that the system will enhance proper recommendation of hospitals for patients to get the best possible care required for presented ailments. Hence, the need to filter, prioritize and efficiently deliver relevant information using recommender systems. We design and develop a recommendation model that uses object-oriented analysis and design methodology (OOADM). The system was implemented using PHP, MYSQL and AJAX technology.


[1] Uko E. O., Eke B.O., Asagba P.O. (2018). An Improved Online Book Recommender System using Collaborative Filtering Algorithm. International Journal of Computer Applications (0975 – 8887) 179(46).
[2] Melville, P., Mooney, R. J., and Nagarajan, R. (2002). Content-boosted collaborative filtering for improved recommendations. In Proceedings of the eighteenth national conference on artificial intelligence (AAAI-02), Edmonton, Alberta, 187-192.
[3] Su, X., and Khoshgoftaar T. M. (2009). A survey of collaborative filtering techniques. Advances in artificial intelligence. Hindawi publishing corporation. 1-19.
[4] Lin W. (2002), “Efficient Adaptive-Support Association Rule Mining for Recommender Systems,” 83–105.
[5] Sarwar, B., Karypis, G., Konstan, J., and Reidi, J. (2001). Item-based collaborative filtering algorithms. GroupLens research group, Army HPC research center, University of Minnesota, Minneapolis, 1-11.
[6] Desrosier, C., and Karypis, G. (2012). A comprehensive survey of neighbourhood-based recommendation methods. Department of Computer Science and Engineering. University of Minnesota, Mineapolis, USA. 5-33.
[7] Jaakkola, H., & Thalheim, B. (2011). Architecture-Driven Modelling Methodologies. Conference on Information Modelling and Knowledge Bases XXII (p. 98). IOS Press.


Architecture, Assumptions, Content-based, Dataset, Euclidean distance, Hybrid, Interface, Manhattan distance, Recommendation.