Abstract
Blood donations help save millions of lives every year. According to the World Health Organization, almost 120 million blood units are collected each year to help people with various health conditions. But this still doesn't meet demands. Blood cannot be stored indefinitely, making blood unit collection a challenge. Furthermore, even though blood banks run blood donation campaigns regularly, some patients are suffering from the lack of suitable blood types in blood banks. Additionally, finding appropriate donors is another common challenge facing blood banks. In this paper, we have proposed a scheme to improve the performance of blood banks and increase the chance to find suitable blood donors promptly. Besides, our system helps to select an effective target group for blood donation campaigns. The proposed blood bank system is artificial intel-ligence-based; it depends on machine learning algorithms to enhance the efficiency of the process of finding potential blood donors. Additionally, the blood donor database is not limited to people who have provided their information to blood banks as anticipated blood donors. It also includes some people who have never visited blood banks. In the suggested system, a machine learning algorithm classifies people in the database into two groups: people who are more likely to donate their blood and those who are less likely to donate blood. The classification relies on the factors that affect a person's be-havior, such as the education level, work environment, culture, and personality. One added benefit of the system would be encouraging blood donation among previously reluctant blood donors.