Developing Knowledge Based System For Blood Donation And Transfusion Using Data Mining Techniques

Abstract

The purpose of this study is to develop a knowledge-based system for blood donation and transfusion using data mining techniques that support physicians and technicians. To overcome the problems, the researcher used the KDD process model and data mining algorithms to identify blood donors. For this study, the dataset was collected from Jimma Zone Blood Bank which has 12,442 instances and 15 attributes. The classification data mining technique was used and selected algorithms are applied for identifying blood donors. The selected algorithms for the experiment were NaiveBayes, J48, JRip, REPTree, and PART classification algorithms. For the test option, the experiment used k-fold cross-validation and percentage of split using the whole and selected attribute. The accuracy obtained from data mining algorithms using 10-folds cross-validation of test options by whole attributes were, 99.6%, 99.7%, 99.49%, 99.54%, and 99.08% for Naïve Bayes, JRip, PART, J48, and REPTree respectively. The developed model was tested by the same algorithms and the accuracy obtained was 96.7%, 96.78%, 96.22%, 96.5%, and 96.82% respectively. The algorithm with high accuracy was selected for generating rules. Hence, knowledge was acquired from these generated rules and represented using rule-based knowledge representation techniques. A knowledge-based system was developed from acquired knowledge for donation and transfusion purposes. Finally, user acceptance testing was conducted, and a developed knowledge-based system was got an acceptance of 87.2% via domain experts. For future work, the dataset will be collected from the different blood banks and the research will be applicable as a country at the different health centers and will use a hybrid form of the process model for better accuracy. Additionally, the algorithms used will be increased and an attractive user interface will be developed to fulfill users' needs.