James Wang¹ and Ang Li², ¹USA, ²California State Polytechnic University, USA

This paper addresses the problem of predicting future medication models in a database using a powerful AI system [1][2]. The background highlights the importance of accurate predictions in healthcare for effective decision-making and improved patient outcomes. The proposed solution involves the development of an AI model trained on a diverse dataset of historical medication models, incorporating advanced machine learning algorithms and techniques.The key technologies and components of the program include data preprocessing, feature selection, algorithm comparison, and performance evaluation [3]. Challenges encountered during the project, such as data quality and model generalizability, were mitigated through careful data cleaning and fine-tuning of the AI model [4][5]. The application of the system to various scenarios during experimentation demonstrated its robustness and versatility.The most important results include high prediction accuracy, precision, and recall in forecasting future medication models. The system showed promising performance across different patient populations, suggesting its potential for personalized treatment planning and decision-making. The idea presented in this paper offers a valuable solution for healthcare professionals and researchers seeking accurate predictions in medication modeling, facilitating better patient care and optimized treatment strategies.

Medication modeling, AI system, Prediction accuracy