Analysis of Suicides in India—A Study Using the Techniques of Big Data

  • Shruti PradhanEmail author
  • Divyansh
  • Manjusha Pandey
  • Siddharth S. Rautaray
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 41)


The idea behind this study is discovering and presenting the prime factors that affect numbers of suicides in India from the year 2001 to 2012. The features in the study refer primarily to the part of the population which are affected most by suicides. Here, we explore the various profiles of the population and how the profile of an individual is contributing to his/her suicidal behavior. The rate of suicides in India has been increasing since the last decade and only a negligible percentage of decline in the same has been observed. The study reflects suicide as a massive social problem and thus, effective interventions for suicide prevention need to be developed at the earliest. This study employs the use of techniques of data analytics to find and analyze the trends in suicides. Data analytics delivers a rich insight from multiple sources and transactions, to uncover hidden patterns and relationships.

Index terms

Suicides in India Big data Suicide analysis Suicide prevention Big data analytics 



We would like to express our profound gratitude to the Dean of School Of Computer Engineering, KIIT, Dr. Samaresh Mishra for allowing us to proceed with the report and for giving us full freedom to access the lab facilities. Our heartfelt thanks to Dr. Siddharth Swarup Rautaray and Dr. Manjusha Pandey for taking time and helping us through our work. They have been a constant source of encouragement without which the work might not have been completed on time. Their ideas and thoughts have been of great importance.


  1. 1.
    Kwon, S., Yang, M.-J., Yoo, J.-H., Kim, L.-S.: Big data analysis of counseling cases for youth at risk of suicide. In: Proceedings of INTCESS15: 2nd International Conference on Education and Social Sciences, 2–4 Feb 2015Google Scholar
  2. 2.
  3. 3.
    Selva Priyanka, S, Galgali, S., Selva Priya, S., Shashank, B.R., Srinivasa, K.G.: Analysis of suicide victim data for the prediction of number of suicides in India. In: IEEE International Conference on Circuits, Controls, Communications and Computing (I4C), 4–6 Oct 2016, pp. 1–5. Electronic-ISBN: 978-1-5090-5369-8Google Scholar
  4. 4.
    Jiang, N., Wang, Y., Sun, L., Song, Y., Sun, H.: An ERP study of implicit emotion processing in depressed suicide attempters. In: 7th International Conference on Information Technology in Medicine and Education (ITME), pp. 37–40 (2015). Electronic ISBN: 978-1-4673-8302-8Google Scholar
  5. 5.
    Berrouiguet, S., Billot, R., Lenca, P., Tanguy, P., Baca-García, E., Simonnet, M., Gourvennec, B.: Toward E-health applications for suicide prevention. In: IEEE First International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE), pp. 346–347 (2016). Electronic ISBN: 978-1-5090-0943-5Google Scholar
  6. 6.
    Yaganteeswarudu, A., Vishnu Vardhan, Y.: Software application to prevent suicides of farmers with MVC. In: 7th International Conference on Cloud Computing, Data Science and Engineering—Confluence, pp. 543–546 (2017). Electronic ISBN: 978-1-5090-3519-9Google Scholar
  7. 7.
    Panda, M., Ali, S.M., Panda, S.K.: Big data in healthcare: a mobile based solution. In: International Conference on Big Data Analytics and Computational Intelligence (ICBDAC), pp. 149–152 (2017). Electronic ISBN: 978-1-5090-6400-7Google Scholar
  8. 8.
    Sikander, D., Arvaneh, M., Amico, F., Healy, G., Ward, T., Kearney, D., Mohedano, E., Fagan, J., Yek, J., Smeaton, A.F., Brophy, J.: Predicting risk of suicide using resting state heart rate. In: Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), pp. 1–4 (2016). Electronic ISBN: 978-9-8814-7682-1Google Scholar
  9. 9.
    Varathan, K.D., Talib, N.: Suicide detection system based on Twitter. In: Science and Information Conference, pp. 785–788 (2014). Electronic ISBN: 978-0-9893193-1-7Google Scholar
  10. 10.
    Vanathi, R., Shaik Abdul Khadir, A.: A robust architectural framework for big data stream computing in personal healthcare real time analytics. In: World Congress on Computing and Communication-Technologies (WCCCT), pp. 97–104 (2017)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Shruti Pradhan
    • 1
    Email author
  • Divyansh
    • 1
  • Manjusha Pandey
    • 1
  • Siddharth S. Rautaray
    • 1
  1. 1.School of Computer Science EngineeringKIITBhubaneswarIndia

Personalised recommendations