IoT data visualizations for hospital insight in corporate finance utilizing machine learning algorithm

Abstract

Traditional hospital property management faces many challenges, such as data silos, real-time query lag, insufficient prediction intelligence, lack of interactivity in visualization, inefficient system integration, and lack of intelligent decision support. These problems affect the accuracy and real-time performance of hospital equipment maintenance, resource allocation, and financial management. To address these issues, this paper uses the Long Short-Term Memory (LSTM) algorithm to construct an intelligent hospital financial management system. The system collects hospital property data in real-time through Internet of Things (IoT) equipment, and uses the LSTM model for analysis and prediction to achieve real-time query and optimization of equipment status, resource consumption, and financial expenditure, thereby improving the visualization of property data. The study trains the LSTM model to analyze real-time data, and then constructs a management system through a modular structure. The comparative experimental findings demonstrate that the constructed system is superior to other similar systems in performance, economic benefits, and stability. The equipment prediction accuracy and recall rate are 92.5% and 88.4% respectively, and no failures occur during the experiment. Therefore, the intelligent financial management solution proposed in this paper has strong practicality and promotion value.