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Fazel Asl N, Ghaffari F, Nasiripour A A. Determining Factors on Hospital Discharge Process Via Data-Mining Method Administered at Shahid Modares Hospital, Tehran. payavard 2018; 11 (5) :509-517
URL: http://payavard.tums.ac.ir/article-1-6387-en.html

Background and Aim: Over the recent years, patient discharge process time has been an important issue focused by so many officials. Therefore, the present study is aimed to identify the main factors with regard to the discharge process and selecting the best data-mining algorithm. 
Materials and Methods: The population in question is all the patients discharged from Modarres Hospital during the first three months in the year 2014. Sampling wasn’t carried out but the number of observations has reached over 1060. Data was gathered via the researcher’s checklist while the relation between dependent and independent variants was examined and identified through T-test, Pearson Correlation Test and one-way analysis of variance. Data Mining Algorithms, in this study, were as follows: Neural Network, Support Vector Machine, Decision Tree, Simple Linear Regression.
Results: The average discharging process in the present study was 246.96 ± 3.25, which shows that among main factors concerned with discharging process, bedridden ward is considered as the most crucial. Also, according to the algorithms employed in this study, Decision Tree, with Correlation Value=0.30 and Root-Mean Square Error=103.29, was the best algorithm.
Conclusion: Results show that Data-Mining Algorithms can be employed to identify crucial factors regarding the whole discharging process and the most important factor during discharge process variable is hospitalization.