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Various data mining techniques have been used extensively by researchers for classification, prediction, clustering, finding association and summarization tasks in the healthcare field. One of the unsupervised data mining techniques named k-means clustering, has been used to categorize the blood donors based on the gender, age, weight and blood group. The authors, Ramachandran et. al. (2011), have used the datasets from Indian Red Cross Society Blood Bank. A system has been developed by ChanLee and Cheng (2011) that uses classification and clustering algorithms to determine the variations in blood donation behaviour amongst the present donors and envisage their intents towards donation so as to understand various matters and to increase the voluntary blood donation frequency. The authors have applied clustering technique to create four groups and have found that the best accuracy is 0.783.
In order to understand the awareness and attitude of students of Semnan university of medical sciences, a descriptive analytical approach has been used by Majdabadi et al. (2018). It was found that a large number of students are not aware of blood donation and possess a negative attitude towards blood donation.
In order to help the humanity and save precious lives, a web-based system for maintaining records of blood donors has been created by Khan et al. (2009). The system registers the donors and keeps their record that has details of blood donors’ blood groups, address for communication, and status of blood donation. This web-enabled system acts as an interface between donors and receptors. Similar web enabled systems have been developed and deployed by Arif et al. (2012) and Guangpeng et al. (2009). With the wide spread usage of mobile communication technologies, a few notification based systems have also been deployed by Singh et al. (2007), Rahman et al. (2011), Samsudinnet al. (2011) and Islam et al. (2013).