Detection of Persistent vs Non-Persistent Medications in Pharmacy Using Artificial Intelligence: Development of Intelligent Algorithms for Pharmaceutical Product Safety
Abstract
The pharmaceutical industry requires an effective system to detect medications that are persistent and non-persistent, in order to improve safety and the efficiency of product management. This study aims to develop a system based on Artificial Intelligence (AI) using the Decision Tree algorithm to classify medications based on prescription data provided by doctors. The dataset used in this study includes prescription information, such as medication type, prescription quantity, frequency of use, and duration of medication use, which are used to determine whether the medication is persistent or non-persistent. The Decision Tree algorithm is applied to develop a reliable classification model, with the goal of detecting medications that are used continuously (persistent) and those that are not used on a continuous basis (non-persistent). This study applies AI technology in the pharmaceutical field, focusing on the use of doctor prescriptions and classifying medications based on usage characteristics. The results of the study show that the algorithm performs well with an accuracy of 78.33%, recall of 0.7804, precision of 0.7804, and an F1 score of 0.6934, indicating the model's ability to classify medications with reasonable accuracy.
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