Drug Recommendation based on drug details and Optimization using Bee Algorithm
Kornpong Ariyachaipong, Pattayapon Senaluang, Pokpong Songmuang, Rachada Kongkachandra · 2024
In an effort to bolster the healthcare system in Thailand, particularly in remote areas with limited access to pharmacists, this study proposes a novel drug recommendation system based on drug details. This system aims to address the challenge of medication selection in resource-constrained settings by providing physicians with informed recommendations tailored to patient needs.The proposed system utilizes drug data sourced from 1mg.com, encompassing approximately 34,284 drug entries. To ensure high-quality recommendations, the data undergoes a rigorous preprocessing phase involving null value imputation and feature extraction using techniques like TF-IDF. Following preprocessing, a combination of Drug Recommendation with cosine similarity and the Bee Algorithm is employed. Cosine similarity establishes a baseline for drug similarity, while the Bee Algorithm optimizes the selection process by considering additional factors beyond just similarity, such as potential side effects and cost-effectiveness.