Blood type identification is a method used for determining the specific blood type of a person. It is a requirement before blood transfusions or blood donations is undertaken especially during emergency situations. Presently, the tests are performed manually by medical technologists in the laboratories. Sometimes, manual blood typing is prone to human error, resulting to incorrect blood grouping and wrong typing in the report, leading to fatal transfusion reactions. The study was focused on the development of a device that is capable of identifying the blood type of an individual using an image processing and machine learning algorithms. The study covered the identification of eight blood types, specifically rhesus positive and negative, A, B, O, and AB, by developing a capturing box integrated with a web camera system that could effectively capture blood sample images. In this study, the methodologies utilized were image processing through segmentation, feature extraction by color and texture properties, and different machine learning algorithms. After training, the results showed that coarse tree DT has the best performance accuracy score of 97.77% using 70:30 holdout validation. The testing results showed that the system is 100% accurate as validated by a registered medical technologist.

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