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In this paper, we worked on methods to reduce the input data set to the mathematical models developed to simulate blood flow through human arteries. In general, any mathematical model designed to mimic a natural process needs specific information on its model parameters. In our models, the inputs to these parameters are from the human arterial system, i.e., the anatomical data on arteries and physiological information on blood. Besides, few other parameters in the models describe mechanisms, such as the arteries' elastic behaviour. These mechanisms described using mathematical relations help assign values to the parameters satisfying specific mathematical requirements. In general, the clinical data or the mathematical requirements provide a range of permissible values for the model parameters. However, we assign only a finite number of values in the range to the parameters to carry out simulations. Even though the values assigned to the parameters significantly differ from each other in magnitude, there is a possibility that some of these data sets mimic the same state of the system (arterial system). And identifying such data sets is not an apparent task but requires robust procedures. Thus, in this work, we attempt to shed light on a data size reduction technique to identify all such values and remove them from the input data set. We propose the statistical testing procedures to identify a significant difference in the dependent variables' values (computed using the developed mathematical models) with the independent variables (the model parameters). Key-Words: Mathematical model, elastic pipe, arterial blood flow, simulation, non-parametric test, statistical analysis. Received: January 22, 2021. Revised: February 26, 2021. Accepted: March 10, 2021. Published: March 18, 2021.

On Refining the Input Data set to Mathematical Models Simulating Arterial blood flow in Humans