Recent advancements in communication technologies, on the one hand, and computer hardwarernand database technologies, on the other hand, have made it easy for organizations to collect, storernand manipulate massive amounts of data. As stated by Deogan, these large databases containrnpotential gold mine of valuable information, but it is beyond human ability to analyze substantialrnamounts of data and extract meaningful patterns. As the volume of data increases, the proportionrnof information in which people could understand decreases substantially. The applications ofrnlearning algorithms in knowledge discovery are promising and they are relevant area of researchrnoffering new possibilities and benefits in real-world applications such as blood bank datarnwarehouse. The availability of optimal blood in blood banks is a critical and important aspect inrna Blood transfusion service. Blood banks are typically based on a healthy person voluntarilyrndonating blood used for transfusions. The ability to identify regular blood donors enables bloodrnbank and voluntary organizations to plan systematically for organizing blood donation camps inrnan efficient manner.rnThe objective of this study is to explore the immense applicability of data mining technology inrnthe Ethiopian National Blood Bank Service by developing a predictive model that could help inrnthe donor recruitment strategies by identifying donors that are at risk of TTIâ€™s which can help inrnthe collection of safe blood group which in turn assists in maintaining optimal blood.rnThe analysis has been carried out on 14575 blood donorâ€™s dataset that has at least one pathogenrnusing the J48 decision tree and NaÃ¯ve Bayes algorithm implemented in Weka. J48 decision treernalgorithm with the overall model accuracy of 89 % has offered interesting rules