Assessment of Blood Donors Using Big Data Analytics

  • R. B. AarthinivasiniEmail author
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 35)


Big data refers to collection of enormous amount of digital information. Blood donation services are crucial for saving lives of many people. Now a day’s need for hygienic blood is increasing very swiftly due to increase in hospitals and increase in disease affected patients. In many cases, urgent need for blood emerges due to some accidents, surgeries etc. Only 5% of Indian population donates blood even though the requirement for blood is very high. Many advertisements and awareness programs are conducted worldwide to make people consciousness about the need for blood. Usually, blood donation process need lot of time and power from both donor and acceptor since there is no proper information system that allows donors and donation centers to connect efficiently and coordinate with each other to decrease time and power required for blood donation process. The proposed system maintains a database that stores the information about all the donors and it will be useful for the patients who require it during emergency situations. This work aims at developing a blood donor assessment system based on the big data. In proposed system the information about the donors stored in database and imported using sqoop tool and processed using hadoop. Donor details that are gathered from the exact locations are visualized on the website. The request and response for the particular blood group send through message. Accordingly, donors can be directed to the nearest location to save the life of the needed people.


Hadoop Mapreduce PCA K-means clustering 


  1. 1.
    Bhardwaj, A., Sharma, A., Shrivastava, V.K.: Data mining techniques and their implementation in blood bank sector – a review. Int. J. Eng. Res. Appl. 2, 1303–1309 (2012)Google Scholar
  2. 2.
    Santhanam, T., Sundaram, S.: Application of CART algorithm in blood donors classification. Int. J. Comput. Sci. 6(5), 548 (2010)Google Scholar
  3. 3.
    Rani, S.A., Ganesh, S.H.: A comparative study of classification algorithm on blood transfusion. Int. J. Adv. Res. Technol. 3(6), 57–60 (2014)Google Scholar
  4. 4.
    Dhoke, N.W., Deshmukh, S.S.: To improve blood donation process using data mining techniques. Int. J. Innovative Res. Comput. Commun. Eng. 3(5) (2015)Google Scholar
  5. 5.
    Boonyanusith, W., Jittamai, P.: Blood donor classification using neural network and decision tree techniques. In: Proceedings of the World Congress on Engineering and Computer Science, vol. 1, pp. 24–26 (October 2012)Google Scholar
  6. 6.
    Chinnaswamy, A., Gopalakrishnan, G., Pandala, K.K., Venkata, K.P., Natarajan, S.: A study on automation of blood donor classification and notification techniques. Int. J. Appl. Eng. Res. 10(7), 18503–18514 (2015)Google Scholar
  7. 7.
    Young, G.O.: Synthetic Structure of Industrial Plastics. In: Peters, J. (ed.) Plastics, vol. 3, 2nd edn, pp. 15–64. McGraw-Hill, New York (1964)Google Scholar
  8. 8.
    Sharma, A., Gupta, P.C.: Predicting the number of blood donors through their age and blood group by using data mining tool. Int. J. Commun. Comput. Technol. 1(6), 6–10 (2012)Google Scholar
  9. 9.
    Dhond, S., Randhavan, P., Munde, B., Patil, R., Patil, V.: Android based health application in cloud computing for blood bank. Int. Eng. Res. J. (IERJ) 1(9), 868–870 (2015)Google Scholar
  10. 10.
    Khan, J.A., Alony, M.R.: A new concept of blood bank management system using cloud computing for rural area (INDIA). Int. J. Electr. Electron. Comput. Eng. 4(1), 20 (2015)Google Scholar
  11. 11.
    Muralidaran, B., Raut, A., Salve, Y., Dange, S., Kolhe, L.: Smart blood bank as a service on cloud. IOSR J. Comput. Eng. 18(2), 121–124 (2016)Google Scholar
  12. 12.
    Hegde, D., Kuriakose, A., Mani, A.M., Philip, A., Abraham, A.P.: Design and implementation of e-blood donation system using location tracking. Int. J. Innovative Res. Comput. Commun. Eng. 5(5) (2017)Google Scholar
  13. 13.
    Vijayabhanu, R.: An efficient mode of communication for blood donor. Int. J. Eng. Technol. Sci. Res. 4(11) (2017)Google Scholar
  14. 14.
    Mandal, M., Jagtap, P., Mhaske, P., Vidhate, S., Patil, S.S.: Implementation of blood donation application using android smartphone. Int. J. Adv. Res. Ideas Innovations Technol. 3(6) (2017)Google Scholar
  15. 15.
    Marjit, U., Sharma, K., Manda, P.: Data transfers in hadoop: a comparative study. Open J. Big Data 1(2), 34–46 (2015)Google Scholar
  16. 16.
    Hashmi, A.S., Ahmad, T.: Big data mining: tools & algorithms. Int. J. Eng. Sci. 5–6 (2016)Google Scholar
  17. 17.
    Kale, S.A., Dandge, S.S.: A comparative analysis of traditional RDBMS with map reduce and hive for e-governance system. Int. J. Eng. Comput. Sci. 4(4), 11224–11228 (2015)Google Scholar
  18. 18.
    Thusoo, A., Sarma, J.S., Jain, N., Shao, Z.: Hive – a petabyte scale data warehouse using hadoop. In: Proceedings of the 26th International Conference on Data Engineering, pp. 1–6 (March 2010)Google Scholar
  19. 19.
    Fuad, A., Erwin, A., Ipung, H.P.: Processing performance on apache pig, apache hive and MySQL cluster. In: Proceedings of International Conference on Information, Communication Technology and System (2014)Google Scholar
  20. 20.
    Aravinth, S.S., Begam, A.H., Shanmugapriyaa, S., Sowmya, S.: An efficient HADOOP frameworks SQOOP and ambari for big data processing. Int. J. Innovative Res. Sci. Technol. 1(10), 252–255 (2015)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringPanimalar Engineering CollegeChennaiIndia

Personalised recommendations