The availability of safe and sufficient blood supply is a cornerstone of any robust healthcare system. In Malaysia, however, blood donation rates remain suboptimal compared to regional counterparts, with logistical inconveniences and fragmented information systems contributing to donor attrition. This project proposes the development of an integrated, web-based platform—Online Blood Donation Manage System with Machine Learning for Good Health and Well-Being—to address these challenges and promote regular blood donation.
The system aims to centralize key functionalities such as donor registration, eligibility screening, donation history tracking, and a reward-based incentive mechanism using donate points. These points can be redeemed for gifts and are valid for two years, strategically designed to reduce the 15.8% lapse rate among donors who have not donated in over two years. Hospitals can manage blood inventory and donor outreach more efficiently, while donors benefit from a streamlined, user-friendly experience.
Machine learning algorithms will be employed to analyze donor behavior, predict donation likelihood, and personalize engagement strategies. By leveraging data from the National Blood Centre and user feedback, the system will continuously evolve to meet public health needs. Ultimately, this project supports the United Nations Sustainable Development Goal 3: Good Health and Well-Being, by fostering a culture of regular blood donation through digital innovation and data-driven engagement.

Invention Technical Description

The Online Blood Donation Manage System is a modular, web-based application designed to optimize blood donation workflows and enhance donor engagement through intelligent automation. It comprises two primary user roles: hospital administrators and blood donors.
System Architecture
- Frontend: Developed using React.js for dynamic user interfaces.
- Backend: Node.js with Express framework for RESTful API services.
- Database: MongoDB for flexible data modeling of donor profiles, donation records, and reward transactions.
- Machine Learning Module: Python-based predictive models using scikit-learn to analyze donor lapse patterns and recommend targeted outreach.
Key Modules
- Donor Portal: Registration, eligibility checklist, donation history, donate points tracker, and gift redemption.
- Hospital Dashboard: Blood inventory management, donor analytics, and campaign scheduling.
- ML Engine: Predictive analytics for donor retention, clustering of donor types, and recommendation system for personalized incentives.
Workflow Optimization
- Donors complete pre-screening online, reducing on-site processing time.
- Eligibility criteria are dynamically updated based on Ministry of Health guidelines.
- Upon successful donation, points are automatically credited and tracked.
- Redemption activities are available year-round, with expiry alerts for unused points.
Evaluation & Testing
- Usability testing with real users to assess interface intuitiveness and process efficiency.
- A/B testing of incentive models to determine optimal reward structures.
- Feedback loop integrated for continuous improvement.
This system not only digitizes the blood donation process but also applies machine learning to enhance donor retention and operational efficiency. It is scalable for nationwide deployment and adaptable to future public health initiatives.

Demostration/ Presentation Video

Poster/ Broucher/ Invention Photo