Comparative Study of Multi-Armed Bandit Algorithms in Clinical Trials

In recent years, with the rapid development of the information age, the influence of Multi Armed Bandit Algorithms (MAB) models in clinical trials for disease prevention has been increasing. In this study, based on Python programming language, Multi-Armed Bandit Algorithms (MAB) algorithm, Upper Confidence Bound (UCB) algorithm, Adaptive Epsilon-Greedy Algorithm, and Thompson Sampling (TS) algorithms to validate the idea of preventing, controlling and predicting the occurrence of diseases. The results show that the MAB model can effectively solve various decision-making problems in clinical trials, improve the efficiency of access to medical care, save doctors diagnosis time, and at the same time achieve the prevention and treatment of diseases while minimising patients pain. This study is dedicated to proposing a more effective decision-making method and verifies that the method has a wide range of applications and great potential for development today.