The Predictive Analysis Method for Maintenance Demand of Multi-Parameter Monitor Based on Machine Learning
Objective To analyze the quality control data set of multi-parameter monitor by using machine learning(ML)algorithm to establish a prediction system,and to explore the prediction effect of the maintenance demand of multi-parameter monitor in the next quality control cycle.Methods With the 1500 multi-parameter monitor quality control data from hospitals from January 2020 to December 2023 screened as the original data set,these data were divided into training and testing sets in an 8:2 ratio,with 1200 training sets and 300 testing sets.The feature selection was carried out for the training set data set,and four groups of sub-datasets were generated.With the application of five ML algorithms:naive bayes(NB),decision tree(DT),random forest(RF),k-nearest neighbor(k-NN)and support vector machine(SVM)to establish a prediction system,the prediction of maintenance needs for multi-parameter monitors in the next quality control cycle was carried out.Results Based on the actual number of multi-parameter monitors that need to be maintained,the average accuracy of the five ML algorithms in predicting the need to maintain the multi-parameter monitor was 96.73%,with a true negative rate of 98.00%.Conclusion The application of ML algorithm can effectively predict the maintenance requirements of multi-parameter monitors in the next quality control cycle,providing a new solution for the maintenance management of multi-parameter monitors.
Machine learningQuality control cycleMulti-parameter monitorMaintenance management
NETL
NSTL
万方数据
