Blood Donation Classification with Decision Tree Method using C4.5 Algorithm - Pangaribuan, Jefri Junifer; Putra, Alexander (International Journal of Multidisciplinary Approach Research and Science, 2024)
Donating blood is an altruistic act driven by concern for others and personal commitment to health. It is crucial for patients needing transfusions due to excessive bleeding. However, there has been a decline in blood donations globally. To address this, the medical community needs a method to predict whether a donor will donate again, enabling proactive measures to ensure an adequate blood supply. This study utilizes data from the University of California, Irvine (UCI) Machine Learning Repository, specifically the Blood Transfusion Service Data Set, employing the Decision Tree method with the C4.5 algorithm. C4.5, an improvement over Iterative Dichotomiser 3 (ID3), can handle missing values, pruning, and continuous data. The aim is to classify blood donor eligibility accurately. The aim of this study is to explore how the utilization of the C4.5 algorithm in decision tree classification can predict whether an individual will donate blood again or not. The analysis identifies five key attributes—Recency, Frequency, Monetary, Time (Months), and Decision—as determinants of repeat donation likelihood. Using a confusion matrix to assess accuracy, the C4.5 algorithm achieved a 77.68% accuracy, with an error rate of 22.32%, a sensitivity of 30.19%, and a specificity of 92.40%.