Explainable Recommendations of Drugs for Diabetic Patients

Priscila Valdiviezo-Diaz

2023

Abstract

Currently, recommender systems are widely used for different purposes, for example, to recommend resources, products, and services. In the health domain, recommender systems are being used to recommender drugs, treatments, food plans, and healthcare services in general. Collaborative filtering is the most popular technique in the recommender system area. This technique can be of two types: memory-based collaborative filtering and based-model collaborative filtering. One of the problems of recommender systems is that most of them focus on enhancing the precision of the recommendation and do not provide a justification for the suggestions given to the user. Therefore, it is important to provide explainable recommendations so that the user understands why an item is recommended. To address this problem, in this paper the use of a Bayesian method for explainable drug recommendations for diabetic patients is presented. Several experiments are carried out using a dataset with information on diabetic patients with three collaborative filtering approaches: the memory-based approach IbCF, and two model-based approaches: item-based NBCF, and Hybrid NBCF. The experimental results present good results for the Hybrid NBCF approach compared to the other approaches tested. Moreover, it is observed a better quality of prediction and an increase in recommendation precision with Hybrid NBCF.

Paper Citation

in Harvard Style

Valdiviezo-Diaz P. (2023). Explainable Recommendations of Drugs for Diabetic Patients. In Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART, ISBN 978-989-758-623-1, pages 73-80. DOI: 10.5220/0011617400003393

in Bibtex Style

@conference{icaart23,
author={Priscila Valdiviezo-Diaz},
title={Explainable Recommendations of Drugs for Diabetic Patients},
booktitle={Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,},
year={2023},
pages={73-80},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011617400003393},
isbn={978-989-758-623-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,
TI - Explainable Recommendations of Drugs for Diabetic Patients
SN - 978-989-758-623-1
AU - Valdiviezo-Diaz P.
PY - 2023
SP - 73
EP - 80
DO - 10.5220/0011617400003393