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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Volume 11 | Issue 3
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Impact Factor : 8.14
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| Paper Title: | B POSITIVE - AN AI BASED BLOOD DONATION SYSTEM |
| Authors Name: | Lipeka Dhamodharan , N Sujatha , Meghan Gowsick S , Mohanraj S |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_207292
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| Published Paper Id: | IJRTI2511034 |
| Published In: | Volume 10 Issue 11, November-2025 |
| DOI: | |
| Abstract: | The goal of the AI-powered blood donation and management ecosystem B Positive is to establish a safe, smart, and open link between patients, donors, and medical facilities. B Positive combines cloud computing, large language models (LLMs), and machine learning to automate donor eligibility verification, fraud detection, and donor-patient matching, in contrast to conventional blood donation systems that depend on manual coordination and static databases. The system predicts eligibility based on lifestyle and physiological factors using a stacked ensemble model (CatBoost, XGBoost, and LightGBM) trained on synthetic donor datasets, with an accuracy of 99.75% (AUC = 1.0). In addition, a Nemotron LLM-based analyzer analyzes hematology reports that have been uploaded, detects abnormal parameters, and offers natural language diagnostic reasoning. Before approving a donor, the report analyses and questionnaire-based predictions are cross-validated. Firebase Firestore powers the backend, guaranteeing scalable, real-time donor and patient record storage. Based on blood group compatibility and availability, a geospatial recommendation engine that was developed with GeoPy and the Haversine formula automatically matches patients with eligible donors within a 50 km radius. OCR-based report verification, consent monitoring, and AI-driven anomaly detection to identify fabricated or altered reports are further layers. Only donors who have been confirmed as "Eligible" by both AI and LLM models are kept in the active collection, according to the system, and cases that are not eligible are redirected with justifications. In addition to streamlining blood donation procedures, B Positive's integrated architecture reduces medical risk, increases transparency, and expedites emergency response. Future additions to further democratize access to safe and effective blood donation across emerging healthcare networks include multilingual NLP support, blockchain-based traceability, and IoT-driven donor monitoring. |
| Keywords: | AI in Healthcare, Blood Donation Management, Donor Eligibility Prediction, Machine Learning, Ensemble Learning, Large Language Models (LLM), Nemotron, Firebase Firestore, Geospatial Recommendation, Medical Report Analysis, Data Verification, Anomaly Detection, Healthcare Automation, Cloud Computing, Ethical AI, Patient Safety. |
| Cite Article: | "B POSITIVE - AN AI BASED BLOOD DONATION SYSTEM", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 11, page no.a250-a257, November-2025, Available :http://www.ijrti.org/papers/IJRTI2511034.pdf |
| Downloads: | 000251 |
| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2511034
Registration ID:207292
Published In: Volume 10 Issue 11, November-2025
DOI (Digital Object Identifier):
Page No: a250-a257 Country: Coimbatore, Tamil Nadu, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2511034 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2511034 |
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ISSN: 2456-3315
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