Application of a hybrid data mining model to identify the main predictive factors influencing hospital length of stay

Abstract

Length of hospital stay is one of the most appropriate measures that can be used for management of hospital resources and assistant of hospital admissions. The main predictive factors associated with the length of stay are critical requirements and should be identified to build a reliable prediction model for hospital stays. A hybrid integration approach consisting of fuzzy radial basis function neural network and hierarchical genetic algorithms was proposed. The proposed approach was applied on a dataset collected from a variety of intensive care units. We achieved an acceptable forecast accuracy level with more than 80.50%. We found 14 common predictive factors. Most notably, we consistently found that the demographic characteristics, hospital features, medical events and comorbidities strongly correlate to the length of stay. The proposed approach can be used as an effective tool for healthcare providers and can be extended to other hospital predictions.