ML Ontological Solution for Blood Donation System

  • Sana Rizwan
  • Muhammad Salman Ahmed
  • Akash ur Rehman
  • Muhammad Abu Hurairah
Keywords: RDF, Ontology, Classes, SPARQL, Object Properties, Data Properties, OWL Wiz, Logistic Regression, SVM, Decision Trees, KNN, Gradient Roasting, Random Forest Classifire, MLP Classifier


Blood donation is basically a process where an individual can voluntarily donate his/her blood for future transfusions. He/she donates his/her blood to the organizations that require that blood for treatment purposes like hospitals etc. People are majorly focused on helping others in case of emergency circumstances. Aim of this working is to provide a platform where they can donate their blood and can seek help in any case of emergency. To solve this real-world problem, adopt machine learning and ontology learning approaches to make the system learn by themselves. Automated blood donation system will use in emergency and make the blood donation an easy process by facilitating people with no hassle in search of blood pints from blood banks. Patients can achieve the blood by contacting the donor through the internet or a personal contact number. This facilitation process starts from blood screening, capturing the data of donors from datasets, adding new donors/donor agencies, applying machine learning algorithms to filter the required data of donor(s) with respect to screening results and diseases and will end on request completion and feedback statements. As the study on the semantic web is progressing, many domain ontologies are being built on the defined case. Ontologies are built for variety of reasons, provide a specification of a particular domain in an explicit format, ontologies serve as a common vocabulary for different people i.e., stakeholders, to communicate about a specific domain, providing a framework for capturing and sharing the knowledge etc. Ontologies help to integrate data from different sources by particular domain knowledge. The trained dataset will be imported and merged with self-created ontological structure, this schema will check by SPARQL queries and exported to web platform.

How to Cite
Rizwan, S., Ahmed, M. S., Rehman, A. ur, & Hurairah, M. A. (2023). ML Ontological Solution for Blood Donation System . European Journal of Science, Innovation and Technology, 3(1), 238-258. Retrieved from