3335 Retrieval Number: D8118118419/2019©BEIESP DOI:10.35940/ijrte.D8118.118419 Journal Website: www.ijrte.org Published By: Blue Eyes Intelligence Engineering & Sciences Publication A Model for Accurate Prediction of Child Immunization Data for Knowledge Discovery using Bayesian TAN and Naive Bayes Classifiers

  • Sourabh*
  • et al.
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Abstract

Knowledge Discovery in Databases (KDD) is a splendid methodology of discovering knowledge from gigantic databases by using its various stages viz. Data Selection, Data Preprocessing, Data Transformation, Data Mining and Interpretation/Evaluation. Data Mining is a vital sub-process of KDD methodology that is particularly used to apply the various mining algorithms on the data. In the present research paper, the authors have made an attempt to discover new knowledge by classifying the child immunization data of Jammu and Kashmir State of India. The data for the present work was collected from a web portal named as Health Management Information System (HMIS) facilitated by Ministry of Health and Family Welfare (MoHFW), Government of India. The data consists of diverse health parameters pertaining to the immunization of children and for the present study, the child immunization data of all districts of Jammu and Kashmir State was considered. Two classifiers viz. Bayesian TAN and Naïve Bayes were employed for classifying the districts of Jammu and Kashmir State into High IMR and Low IMR districts based on the available past data from 2014 to 2018. Additionally, various measurement methods have been used to evaluate the performance of the models developed by Bayesian TAN and Naïve Bayes.

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Sourabh*, & Mansotra, P. V. (2019). 3335 Retrieval Number: D8118118419/2019©BEIESP DOI:10.35940/ijrte.D8118.118419 Journal Website: www.ijrte.org Published By: Blue Eyes Intelligence Engineering & Sciences Publication A Model for Accurate Prediction of Child Immunization Data for Knowledge Discovery using Bayesian TAN and Naive Bayes Classifiers. International Journal of Recent Technology and Engineering (IJRTE), 8(4), 3335–3343. https://doi.org/10.35940/ijrte.d8118.118419

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