Data Mining Probabilistic Classifiers for Extracting Knowledge from Maternal Health Datasets
Sourabh1, Vibhakar Mansotra2
1Sourabh*, Ph.D. from Department of Computer Science & IT, University of Jammu.
2Vibhakar Mansotra, Professor in Department of Computer Science and IT, University of Jammu.
Manuscript received on November 13, 2019. | Revised Manuscript received on 24 November, 2019. | Manuscript published on December 10, 2019. | PP: 2769-2776 | Volume-9 Issue-2, December 2019. | Retrieval Number: B6633129219/2019©BEIESP | DOI: 10.35940/ijitee.B6633.129219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Data Mining is an important sub-process of Knowledge Discovery in Databases (KDD) or Knowledge Discovery Process (KDP) methodology that is mainly used for applying various data mining techniques and algorithms on the target data. In this research paper, the authors have made an attempt to discover knowledge by classifying the maternal healthcare data of Jammu and Kashmir State of India (now declared as Union Territory by the Government of India). The data for the present research 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 maternal health of women and for this study, the maternal healthcare data of all districts of Jammu and Kashmir State was considered. Two data mining classifiers viz. Bayesian TAN and Naïve Bayes were applied for classifying the districts of Jammu and Kashmir State into High MMR and Low MMR districts based on the available past data from 2014 to 2018. Additionally, evaluation measures viz. Accuracy, F-measure, Area under the Curve (AUC), and Gini have been used to evaluate the performance of the models developed by Bayesian TAN and Naïve Bayes.
Keywords: Data Mining, KDD, Classification, Bayesian TAN, Naïve Bayes, Maternal Health, Accuracy, F-measure, AUC, Gini
Scope of the Article: Data Mining