Health Professions
6 years ago

New classification model speeds up blood donor eligibility process by 15%!

2018 6th International Conference on Cyber and IT Service Management (CITSM)
An Approach to Classify Eligibility Blood Donors Using Decision Tree and Naive Bayes Classifier
Wildan Budiawan Zulfikar, Yana Aditia Gerhana, Aulia Fitri Rahmania

Paper Summary

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The article discusses a new method to quickly determine if someone is eligible to donate blood. The researchers used decision tree and naive bayes classifier algorithms to classify blood donors based on factors like blood type, age, and hemoglobin levels. They found that the naive bayes classifier was more accurate, with a 79.95% success rate compared to the decision tree's 66.65%. In testing with 100 data points for testing and 400 for training, the naive bayes classifier had an accuracy of 81.5% while the decision tree had 78.5%.