Abstract
This vital health phenomenon raises problems related to identifying medicinal products and medical equipment that are most frequently prescribed by specialist doctors, and are in demand by patients, as well as efficient stock management. The main challenge faced by hospitals is the difficulty in predicting which medicines and health devices are most in demand. This research analyzes and predicts the best-selling medicines and medical devices based on historical sales and demand data. By adopting a machine learning approach using the K-Nearest Neighbors (KNN) algorithm, research can help hospitals optimize services, especially the availability of stock of medicines and health equipment. The analysis results provide deep insight into patient preferences and demand trends by specialist doctors, enabling smarter stock management adjustments. It is hoped that this solution will reduce stock shortages and waste of storage resources, contributing to more efficient healthcare services. In conclusion, this research shows that the KNN algorithm can provide intelligent solutions to overcome complex challenges in managing valuable health resources.
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