ABSTRACT
Objective To propose a decision tree-based early warning model so as to predict the medical equipment quantity and quality under special conditions to quantize maintenance and detection staffs allocation,components supply,emergency planning and etc.Methods A data set was established based on the verification report,data on performance parameters detection and daily management log,and then underwent pretreatment.A decision tree came into being with the algorithms of ID3,CHAID and etc.The data rule corresponding to the decision tree was transformed into a series of early warning information.The model was verified by applying it to medical equipment usage management.Results The decision tree developed was applied to analyzing a vehicle-mounted water purifying device maintenance and usage records in some logistics support vehicle,and it's found the main factors contributing to the failures included migration distance along non-paved road,ambient temperature and service time.Conclusion The decision tree-based early warning model for medical equipment quantity and quality gains high feasibility and practical values.