Application of rule-based data mining in extracting the rules from the number of patients and climatic factors in instantaneous to long-term spectrum

Predicting the number of patients helps managers to allocate resources in hospitals efficiently. In this research, the relationship between the number of patients with the temperature, relative humidity, wind speed, air pressure, and air pollution in instantaneous, short-, medium- and long-term indices was investigated. Genetic algorithm and ID3 decision tree have been used for feature selection, and classification based on multidimensional association rule mining algorithm has been applied for rule mining. The data have been collected for 19 months from a pediatric hospital whose wards are nephrology, hematology, emergency, and PICU. The results show that in the long-term index, all climatic factors are correlated with the number of patients in all wards. Also, several if-then rules have been obtained, indicating the relationship between climate factors in four indices with the number of patients in each hospital ward. According to if-then rules, optimal planning can be done for resource allocation in the hospital.