Ischemic Heart Patient's Length of Stay Estimation and Identification of its Influencing Factors Using Data Mining

Ischemic Heart Disease (IHD) is one of the costly and controversial topics in the field of healthcare in Iran. Due to limitation in hospital resources for patient care, studying patient’s length of stay (LOS) is very important in hospital management. This study presents suitable models for estimating the LOS of IHD patients and its influencing factors.
In this applied research, LOS of 6524 IHD in-patients admitted to 16 hospitals in Tehran, Iran, between October 2013 and March 2014 and the remedial proceedings provided for them were recorded using treatment services tariff codes. After data collection using a predesigned form and data cleaning, LOS fort models were created using data mining algorithms of artificial neural network (ANN), support vector machines (SVM), chi-squared automatic interaction detection (CHAID), classification and regression trees (CART), and ensemble model in SPSS Clementine software.
The average and standard deviation of LOS in this study was 7.727 ± 9.608 days. The linear correlation of models with actual LOS and their relative error were above 0.7 and below 0.5, respectively. The most accurate models were the ensemble model and SVM.
According to the proposed models, ischemic patients who required rehabilitation, consultation, radiation therapy, and computerized tomography (CT) scan have longer LOS. Moreover, type of IHD disease and especially California remedial codes are important factors in estimating patients’ LOS.