Efficient organization of health data using modified range based multidimensional R-Trees

The existing organization of data structures for healthcare systems face difficulties as health care applications demand high speed storage and retrieval of data. This is due to the increasing number of parameters involved in algorithms corresponding to healthcare applications and the large amount of data generated by tests under every parameter. Hence, there is a need for a different type of data structures that is suitable for storage and retrieval of data related to healthcare applications. Hence, in this paper we propose a method for organizing data using a modified form of R-Trees namely mR-Trees. Conventional R-Trees deal with only static textual data. mR-Trees has been designed to support multi-dimensional dynamic numerical and textual data by using ranges at every node. Every node in the data structure is constrained to contain a fixed number of elements. If the number of elements exceeds the minimum bound, the ranges get split. This improves the complexity of the data structure from linear to logarithmic. The proposed data structure has been implemented in C++ and proved to be efficient compared to conventional data structures as they consume lesser access time.