Document Type : Short Communication


Department of Mathematics, Umakanta Academy, Agartala-799001, Tripura, India.


Statistics mainly concerned with data that may be qualitative or quantitative. Earlier we have used the notion of statistics in the classical sense where we assign values that are crisp. But in reality, we find some areas where the crisp concept is not sufficient to solve the problem. So, it seems difficult to assign a definite value for each parameter. For this, fuzzy sets and logic have been introduced to give the flexibility to analyze and classify statistical data. Moreover, we may come across such parameters that are indeterminate, uncertain, imprecise, incomplete, unknown, unsure, approximate, and even completely unknown. Intuitionistic fuzzy set explain uncertainty at some extent. But itis not sufficient to study all sorts of uncertainty present in real-life. It means that there exists data which are neutrosophic in nature. So, neutrosophic data plays a significant role to study the concept of indeterminacy present in a data without any restriction. The main objective of preparing this article is to highlighting the importance of neutrosophication of statistical data in a study to assess the symptoms related to Reproductive Tract Infections (RTIs) or Sexually Transmitted Infections (STIs) among women by sampling estimation.


Main Subjects

  1. Atanassov, K. (1986). Intuitionistic fuzzy sets. Fuzzy sets and systems, 20, 87-96.
  2. Durai, V., Varadharajan, S., & Muthuthandavan, A. R. (2019). Reprductive tract infections in rural India-a population based study. Journal of family medicine and primary care, 8, 3578-3583.
  3. Kafle, P., & Bhattarai, S. S. (2016). Prevalence and factors associated with reproductive tract infections in Gongolia village, Rupandehi district, Nepal. Advances in Public Health.
  4. Maji, P. K. (2013). Neutrosophic soft set. Annals of fuzzy mathematics and informatics, 5, 157-168.
  5. Maji, P. K. (2012). A neutrosophic soft set approach to a decision making problem. Annals of fuzzy mathematics and informatics, 3, 313-319.
  6. Mani, G., Annadurai, K., & Danasekaran, R. (2013). Healthcare seeking behaviour of symptoms of reproductive tract infections among rural married women in Tamil Nadu-A community based study. Online journal of health and allied sciences, 12, 1-4.
  7. Mitra Basu, T., & Mondal, S. K. (2015). Neutrosophic soft matrix and its application in solving group decision making problems from medical science. Computer communication and collaboration, 3(1), 1-31.
  8. Naderi, J., Chopra, A., & Relwani, R. (2020). Reproductive tract infection among women suffering from rheumatoid arthritis in India: a clinical-based, cross-sectional study. Jundishapur journal of microbiology, 13. DOI:5812/jjm.97176
  9. Rizwan, S. A., Rath, R. S., & Vivek, G. (2015). KAP study on sexually transmitted infections/Reproductive tract infections (STIs/RTIs) among married women in rural Haryana. Indian dermatology online journal6(1), 9-12.
  10. Sahin, M., Alkhazaleh, S., & Ulucay, V. (2015). Neutrosophic soft expert sets.  Applied mathematics, 6(1), 116-127.
  11. Sangeetha, S. (2012). A study of reproductive tract infections among pregnant women in the reproductive age group, in Urban Field Practice Area in Hubli, Karnataka, India. Annals of tropical medicine and public health5(3), 209-213.
  1. Shingade, P. P., Kazi, Y., & Madhavi, L. H. (2015). Treatment seeking behavior for sexually transmitted infections/reproductive tract infections among married women in urban slums of Mumbai, India. South east asia journal of public health, 5(2), 65-70.
  2. Smarandache, F. (2005). Neutrosophic set-a generalization of the intuitionistic fuzzy sets. International journal of pure and applied mathematics, 24(3), 287–297.
  3. Smarandache, F. (1998). Neutrosophy, neutrosophic probability, set and logic. American Research Press.
  4. Smarandache, F. (2001). Preface: an introduction to neutrosophy, neutrosophic logic, neutrosophic net, and neutrosophic probability and statistics. Proceeding of the first international conference on neutrosophy, neutrosophic logic, set, probability and statistics.
  5. Smarandache, F. (2014). Introduction to neutrosophic statistics. Zendo. DOI: 5281/zenodo.8840
  6. Tas, F., Topal, S., & Smarandache, F. (2018). Clustering neutrosophic data sets and neutrosophic valued metric spaces. Symmetry, 10(10).
  7. Zadeh, L. A. (1965). Fuzzy set. Information and control, 8, 338-353.