On the Study of Risk Factors of Ca. Cervix and Ca. Breast: a Case Study in Assam

Journal Title: Bonfring International Journal of Data Mining - Year 2012, Vol 2, Issue 2

Abstract

Ca.cervix and ca.breast are the most common life threatening cancers among women worldwide and the same is true for north east region of India also. So these two cancers remain a serious public health problem worldwide. Therefore more research work on the risk factors for ca.cervix and ca.breast is needed to better understand its etiology and pathogenesis. With this background this study is conducted to observe the possible factors such as socio-economic, marital etc., which may lead to the occurrence of these cancers in our region. For conducting this study we collect data by interviewing the patient registered in the B. Barooah cancer research institute which is the main sources of cancer data in Assam. Here we take 30 diagnosed cases each for ca.cervix and ca.breast and plot the data in a cross tabular form and analyze different factors and finally we try to abstract the conclusion from these tables. From the study it is revealed that there is strong evidence between the cancer type (ca.breast and ca.cervix) and the following risk factors: family income (p=.017), age at marriage (p=.031), age of the patients (p=.017), number of children (p=.001), age at first child birth (p=.003), oral contraceptive used (p=.028). It has been further observed that most of the patients are house wives and non vegetarian. Moreover it is seen that of the two types of cancer the Bengali population of the state are more afflicted by ca.breast whereas the Assamese population are more afflicted by ca.cervix.

Authors and Affiliations

Lipi B. Mahanta , Dilip C. Nath , Nijara Rajbongshi

Keywords

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  • EP ID EP161570
  • DOI 10.9756/BIJDM.1348
  • Views 109
  • Downloads 0

How To Cite

Lipi B. Mahanta, Dilip C. Nath, Nijara Rajbongshi (2012). On the Study of Risk Factors of Ca. Cervix and Ca. Breast: a Case Study in Assam. Bonfring International Journal of Data Mining, 2(2), 27-31. https://europub.co.uk/articles/-A-161570