An Experimental Study of Data Mining Techniques in Blood Donors Sector

Abstract

In today’s computer age data storage has been growing in size to unthinkable ranges that only computerized methods applied to find information among these large repositories of data available to organizations whether it was online or offline. Data mining was conceptualised in the 1990s as a means of addressing the problem of analyzing the vast repositories of data that are available to mankind, and being added to continuously. Data mining is necessary to extract hidden useful information from the large datasets in a given application. This usefulness relates to the user goal, in other words only the user can determine whether the resulting knowledge answers his goal. The growing quality demand in the blood bank sector makes it necessary to exploit the whole potential of stored data efficiently, not only the clinical data and also to improve the behaviours of the blood donors.