Application of Data Mining Technology in Predicting The Seropre Valence of Hbv, Hcv,Hiv; The Case of The National Blood Bank of Addis Ababa, Ethiopia
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Date
2011-07
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Addis Ababa University
Abstract
Recent advancements in communication technologies, on the one hand, and computer hardwareand database technologies, on the other hand, have made it easy for organizations to collect, storeand manipulate massive amounts of data. As stated by Deogan, these large databases containpotential gold mine of valuable information, but it is beyond human ability to analyze substantialamounts of data and extract meaningful patterns. As the volume of data increases, the proportionof information in which people could understand decreases substantially. The applications oflearning algorithms in knowledge discovery are promising and they are relevant area of researchoffering new possibilities and benefits in real-world applications such as blood bank datawarehouse. The availability of optimal blood in blood banks is a critical and important aspect ina Blood transfusion service. Blood banks are typically based on a healthy person voluntarilydonating blood used for transfusions. The ability to identify regular blood donors enables bloodbank and voluntary organizations to plan systematically for organizing blood donation camps inan efficient manner.The objective of this study is to explore the immense applicability of data mining technology inthe Ethiopian National Blood Bank Service by developing a predictive model that could help inthe donor recruitment strategies by identifying donors that are at risk of TTI’s which can help inthe collection of safe blood group which in turn assists in maintaining optimal blood.The analysis has been carried out on 14575 blood donor’s dataset that has at least one pathogenusing the J48 decision tree and Naïve Bayes algorithm implemented in Weka. J48 decision treealgorithm with the overall model accuracy of 89 % has offered interesting rules
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Mining Technology