aspg-2-2324-1791417475 20261007235755 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Cybersecurity and Information Management JCIM 2769-7851 2690-6775 2024 13 1 Anil Audumbar Pise University of the Witwatersrand Johannesburg, South Africa Ganesh Shivaji Pise Assistant Professor in Pune Institute of Computer Technology Pune, India Saurabh Singh Assistant Professor, Department of AI and Big data, Woosong University, Daejeon, South Korea Hemachandran K. Associate Engineer, TSYS Global Payments, Pune, India Jude Imuede University of Prince Edward Island, Canada Sandip Shinde Department of Computer Engineering, Vishwakarma Institute of Technology, Pune, India The exponential rise in accidents and the introduction of new, supposedly trendy ways of living have contributed to the dire need for the needy to have an organ or blood transfusion. Circumstances refer to a circumstance where proper care should be taken when collecting the necessary blood or original parts for transfusion, typically in dire circumstances. To determine the distance at which the interested and qualified donors are located, a thorough investigation must be conducted. People are often first categorized according to their blood type, eligibility, and region. Following that, people group together according to locality. A healthy person can safely donate blood twice within 56 days, as this is the minimal time between successful donations that has been established as a norm. The decision to donate an organ is often made after careful consideration of the severity of the situation, the donor's health, and the health of the recipient. Knowledge data finding tasks can be made easier with the help of KEEL, an open-source programme. The graphs that are produced show clearly how the proposed algorithm varies from the standard K-means method. Therefore, it will be quite useful in the present day and could end up saving lots of lives. The necessity to decide ahead of time on the total number of groups is just one of the issues with the K-means clustering method. In practise, it is difficult to anticipate the precise number of clusters. When the number of clusters is small, incongruous clustering is more common, but when the number of clusters is large, like clustering is more common. Thanks to a method called Active Cluster with Modified k-means clustering, which finds the right number of clusters on the fly, the issue is now resolved. 2024 08 16 2324 https://creativecommons.org/licenses/by/4.0/ 10.54216/JCIM.130101 https://www.americaspg.com/journal/2/article/2324 https://www.americaspg.com/storage/91740928286.pdf