KDD-Based Decision Making: A Conceptual Framework Model for Maternal Health and Child Immunization Databases

  • Sourabh ShastriEmail author
  • Vibhakar Mansotra
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 924)


This paper focuses on the issues apposite to the use of maternal health and child immunization data and throws light on how the KDD (Knowledge Discovery in Databases) process makes use of maternal health and child immunization data for model building and decision making at various levels in healthcare sector. Data mining techniques and algorithms play a critical and vital role in these types of KDD systems, and the idea of using the same in such models and systems is to build an automated tool for identifying and spreading important healthcare information and knowledge. The implementation of these types of developed models is possibly the much-needed intervention that improves the usability of maternal health and child immunization data as it is used for better decision making by healthcare professionals to develop plans and policies to help the society and to achieve the better outcomes for an effective healthcare management. For these motives, a conceptual framework model based on KDD was designed during the present study to discover knowledge from maternal health and child immunization databases.


Maternal health Child immunization KDD Data mining Decision making 


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© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Computer Science and ITUniversity of JammuJammuIndia

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