A Hybrid Model Based on EMD-Feature Selection and Random Forest Method for Medical Data Forecasting

Abstract

Hospital managers need to allocate emergency department (ED) resources efficiently because of the gradual aging of the population, emergency services overcrowding problem is arising. Forecasting is a vital activity that instructs decision-makers in related research fields, such as industrial scientific planning, economics, and healthcare. Scientists have applied time series methods to daily patient number forecasting at ED. Traditional time series models usually use a single variable for forecasting, but noises caused by weather conditions change and environmental factors would be included in raw data. Low forecasting performance would be generated because of using complicated raw data in time series models. Further, traditional time series models cannot be utilized in all datasets because statistics models need to meet statistical assumptions. Multi-attribute data will usually produce high-dimensional data and increase the computational complexity in the data mining procedure. For overcoming these drawbacks above, this study proposes a hybrid random-forest model based on AR (autoregressive) and empirical mode decomposition (EMD). The proposed model utilizes EMD to decompose complicated raw data into correlations frequency components and uses the feature section method to reduce high-dimensional input data generated by EMD. Then, this study combines random forest method that can surmount the limitations of statistical methods (data need to obey some mathematical distribution) to forecast daily patient volumes. To verification, daily patient volumes in an emergency are collected as experimental datasets to evaluate the proposed model. Experimental results illustrate that the proposed model surpasses the listing models