AI-Powered Medication Reminder and Tracker using Reinforcement Learning Algorithm

The goal of this analysis is to use a reinforcement learning (RL) algorithm to develop an artificial intelligence (AI) based therapeutic reminder system and tracker. To increase monitoring accuracy and facilitate observance, this involves comparing K-Nearest Neighbors (KNN). Two groups were assessed in this study. Group 1 on the other hand, utilized the classical KNN (K-Nearest Neighbors) algorithm for medication tracking, also processing 25 same samples. Group 2 used an AI-powered medication reminder and tracking system, equipped with a reinforcement learning algorithm, and processed 25 samples. The study’s main objective was to calculate and compare the accuracy of these two systems. The G Power value was set at 80%, and the threshold was set at 0.05% with a 95% confidence interval. The accuracy that the reinforcement learning-based medication reminder and tracker AI performed in Group 2 was 92%, which showed a higher accuracy rate than that of Group 1, which used a KNN algorithm and scored 78%. In addition, the AI could respond faster and predict reminder with an improved rate of reliability, hence providing better results. The AI-based medication reminder and tracker system, powered by the reinforcement learning algorithm, significantly outperforms the traditional KNN algorithm in terms of accuracy, speed, and managing complex medication schedules. Its ability to handle a variety of user behaviors and deliver timely reminders ensures a more reliable and efficient medication tracking experience.