Surplus Value Quantification of Overdue Medical Devices Based on Kohonen Network Algorithm

Abstract

Abstract With the continuous updating and progress of medical equipment, overdue medical device has problems such as management difficulties, resource waste and potential security risks. Therefore, this paper used Kohonen network algorithm to quantitatively evaluate and analyze the surplus value of overdue medical devices. In this paper, Kohonen network algorithm was used to build a quantitative model of the surplus value of overdue medical device, and the self-organization characteristics and data-driven learning ability of Kohonen network were used to more accurately predict the surplus value of equipment. Support vector machine was used to quantitatively evaluate and predict the surplus value of overdue medical device, and further optimize the model performance, so as to provide more accurate and reliable decision support for medical equipment management. The Kohonen network algorithm used in this paper well evaluated the correlation between the service life and maintenance cost of eight types of overdue medical device, and quantitatively predicted the surplus value of overdue medical device with the random forest algorithm. According to the comparison of prediction bias, the Kohonen network algorithm in this paper has better prediction performance than the random forest algorithm. In the experiment of comparative analysis and verification by introducing decision tree algorithm, the average error rate of Kohonen network algorithm in this paper was only 20.57%, which was far lower than 46.34% of random forest algorithm and 65.31% of decision tree algorithm. The Kohonen network algorithm used in this paper can effectively quantitatively evaluate and predict the surplus value of overdue medical device, thus improving the efficiency of medical equipment management, reducing costs and ensuring patient safety.