Rule Mining about the Relationship between Climatic Factors and the Number of Patients in a Hospital Using Classification Based on Multidimensional Association Rule Mining

Document Type : Research Paper


1 PhD Candidate, Department of Industrial Management, Faculty of Economic & Administrative Sciences, Ferdowsi University of Mashhad, Mashhad, Iran.

2 Associate Prof., Department of Industrial Management, Faculty of Economic & Administrative Sciences, Ferdowsi University of Mashhad, Mashhad, Iran.

3 Assistant Prof., Department of Industrial Management, Faculty of Economic & Administrative Sciences, Ferdowsi University of Mashhad, Mashhad, Iran.

4 Associate Prof., Department of Artificial Intelligence, Faculty of Computer Engineering, Iran University of Science and Technology, Tehran, Iran.


Objective: There are many climatic factors affecting the number of patients in hospitals which generally tend to make a Non-optimal use of their facilities and human resources.This research is aimed at discovering hidden knowledge between climatic factors and the number of hospital patients using data mining techniques.
Methods: In this study, the relationship between climatic factors and the number of patients in Dr. Sheikh specialized pediatric hospital of Mashhad is investigated by classification based on multidimensional association rule mining. The number of patients in the nephrology, hematology, emergency and PICU department of this hospital have been considered separately, and consequently the relationship between the number of patients and the climatic factors such as air temperature, relative humidity, wind speed, air pressure and air pollution have been analyzed. This research has analyzed data gathered through a 19 month period and has been obtained by referring to the documents. In this research for feature selection, all subsets of climatic factors are searched and the effect of all subsets on the number of patients are evaluated using linear regression. Also for rule mining is used classification based on multidimensional association rule mining which is based on known Apriori algorithm.
Results: The results show different patterns that indicate the relationship between the number of patients in the hospital departments with the climatic factors.
Conclusion: This study is able to help analyze the relationship between the climatic factors and the number of patients in the hospital. Also, the rules will help managers make optimal planning for hospital resources according to the different number of patients.


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