Extract critical factors affecting the length of hospital stay of pneumonia patient by data mining (case study: an Iranian hospital)

The proposed model in the study demonstrates the way various scenarios of data processing impact on the scale efficiency model, which points to the significance of the pre-processing in data mining. In this article, some methods were utilized; it is noteworthy that Bayesian boosting method led to better results in identifying the factors affecting LOS (accuracy 95.17%). In addition, it was found that 58% of patients younger than 15 years old and 74% of the elderly within the age range of 74-88 were more vulnerable to pneumonia disease. Also, it was found that the Meropenem is a relatively more effective medicine compared to other antibiotics which are used to treat pneumonia in the majority of age groups. Regardless of the impact of various laboratory findings (including CRP, ESR, WBC, NA, K), the patients LOS decreased as a result of Meropenem.