IDEAS home Printed from https://ideas.repec.org/a/jbh/ijsrcs/v2y2017i4idhcseit1724136.html

Naive Bayesian Model for Predicting Mode of Delivery

Author

Listed:
  • K. Lakshmi
  • P. Raja Priya
  • T. Nivedhitha

Abstract

Giving birth to a child gives anxiety to any family due to many uncertainties even at the technology era. The research work analyzes the data of pregnant women and brings out a model for predicting the mode of delivery of baby. This paper presents the analytical data & effectiveness of the Bayesian model. We have considered the features such as age, BMI (Body Mass Index), GDM (Gestational Diabetes Mellitus) etc., The purpose of this study was to determine the relative effects of pregnancy and mode of delivery.

Suggested Citation

  • K. Lakshmi & P. Raja Priya & T. Nivedhitha, 2017. "Naive Bayesian Model for Predicting Mode of Delivery," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 2(4), pages 562-565, August.
    • Handle: RePEc:jbh:ijsrcs:v2:y2017:i4:id:hcseit1724136
    Note: Article URL: https://ijsrcseit.com/CSEIT1724136
    as

    Download full text from publisher

    File URL: https://ijsrcseit.com/CSEIT1724136
    File Function: Article URL
    Download Restriction: no
    File URL: https://ijsrcseit.com/paper/CSEIT1724136.pdf
    File Function: Full text
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:jbh:ijsrcs:v2:y2017:i4:id:hcseit1724136. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Pankaj Sharma (USA) (email available below). General contact details of provider: https://ijsrcseit.com/home .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.