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
| dc.contributor.advisor | Meshesha, Million(PhD) | |
| dc.contributor.author | Gebregziabher, Haftom | |
| dc.date.accessioned | 2018-08-06T07:03:34Z | |
| dc.date.accessioned | 2023-11-06T09:01:58Z | |
| dc.date.available | 2018-08-06T07:03:34Z | |
| dc.date.available | 2023-11-06T09:01:58Z | |
| dc.date.issued | 2011-07 | |
| dc.description.abstract | Recent advancements in communication technologies, on the one hand, and computer hardware and database technologies, on the other hand, have made it easy for organizations to collect, store and manipulate massive amounts of data. As stated by Deogan, these large databases contain potential gold mine of valuable information, but it is beyond human ability to analyze substantial amounts of data and extract meaningful patterns. As the volume of data increases, the proportion of information in which people could understand decreases substantially. The applications of learning algorithms in knowledge discovery are promising and they are relevant area of research offering new possibilities and benefits in real-world applications such as blood bank data warehouse. The availability of optimal blood in blood banks is a critical and important aspect in a Blood transfusion service. Blood banks are typically based on a healthy person voluntarily donating blood used for transfusions. The ability to identify regular blood donors enables blood bank and voluntary organizations to plan systematically for organizing blood donation camps in an efficient manner. The objective of this study is to explore the immense applicability of data mining technology in the Ethiopian National Blood Bank Service by developing a predictive model that could help in the donor recruitment strategies by identifying donors that are at risk of TTI’s which can help in the 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 pathogen using the J48 decision tree and Naïve Bayes algorithm implemented in Weka. J48 decision tree algorithm with the overall model accuracy of 89 % has offered interesting rules | en_US |
| dc.identifier.uri | http://etd.aau.edu.et/handle/123456789/10969 | |
| dc.language.iso | en | en_US |
| dc.publisher | Addis Ababa University | en_US |
| dc.subject | Mining Technology | en_US |
| dc.title | 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 | en_US |
| dc.type | Thesis | en_US |