Blood Donor Classifier Using Hybrid Naïve Bayes Decision Tree (HNBDT)

Blood donation plays a critical role in healthcare, ensuring a stable supply of blood for medical treatments and emergencies. Efficiently identifying eligible blood donors is paramount to maintaining this supply. The research paper investigates an approach to automate blood donor classification using the Hybrid Naïve Bayes Decision Tree (HNBDT). The study aims to address the growing need for an accurate and efficient donor eligibility assessment system. The prescribed Algorithm i.e., the Hybrid Naïve Bayes Decision Tree Algorithm (HNBDT) gives an accuracy of 75% compared to the traditional classification algorithms of Naïve Bayes and the Decision Tree (DT). The increased accuracy of HNBDT is due to the major step of hybridization.