Data Mining of Access to Tetanus Toxoid Immunization Among Women of Childbearing Age in Ethiopia

Kedir Hussein Abegaz; Emiru Merdassa Atomssa

doi:doi:10.11648/j.mlr.20170202.12

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Data Mining of Access to Tetanus Toxoid Immunization Among Women of Childbearing Age in Ethiopia

Kedir Hussein Abegaz, Emiru Merdassa Atomssa

Published in Machine Learning Research (Volume 2, Issue 2)

Received: 7 February 2017 Accepted: 21 February 2017 Published: 9 March 2017

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Abstract

Tetanus toxoid (TT) vaccine is given to women of childbearing age to prevent neonatal tetanus and maternal mortality attributed to tetanus. Globally, tetanus is responsible for 5% of maternal deaths and 14% of neonatal deaths annually. Data mining is the process of discovering interesting patterns and knowledge from large amounts of data. Thus, the aim of this study was to identify the best classifier, and to predict the pattern from the TT data set using the data mining algorithms technique. The data for this study were the Tetanus Toxoid data set from the Ethiopian Demographic and Health Survey (EDHS) 2011, and analyzed using the Knowledge discovery process of Selection, Processing, Transforming, mining, and interpretation. The WEKA 3.6.1 tool was used for classification, clustering, association and attribute selection. The accuracy rate of the classifiers on training data is relatively higher than on test data and the multilayer perceptron is the best classifier in our data set on Tetanus toxoid. In the cross-validation with 10 folds, correctly classified best are by naïve Bayesian 63.30% and the least accurate were by k-nearest neighbor 60.52%. Single data instance test using Naïve Bayesian was done by creating test 1, test 2, test 3, and test 4 data test instance, three of them are correctly predicted but one of them incorrectly classified. The maximum confidence attained in the general association is 0.98. But, in the class attribute, it is 0.72. The literacy status of the mother has high information gain with the value 0.046. As a conclusion, the best algorithm based on the TT vaccination data is multilayer perceptron classifier with an accuracy of 67.28% and the total time taken to build the model is at 0.01 seconds. Multilayer perceptron classifier has the lowest average error at 32.72% compared to others. These results suggest that among the machine learning algorithm tested, multilayer perceptron classifier has the potential to significantly improve the conventional classification methods for use in EDHS data of Tetanus toxoid.

Published in	Machine Learning Research (Volume 2, Issue 2)
DOI	10.11648/j.mlr.20170202.12
Page(s)	54-60
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2017. Published by Science Publishing Group

Keywords

Data Mining, WEKA, Classification, Clustering, Tetanus Toxoid (TT), EDHS

References

[1]	Central Statistical Agency (CSA) [Ethiopia] and ICF, Ethiopia Demographic and Health Survey 2016: Key Indicators Report. 2016: Addis Ababa, Ethiopia, and Rockville, Maryland, USA, CSA, and ICF.
[2]	WHO, Maternal immunization against tetanus: Standards for Maternal and Neonatal Care. 2006, Department of making pregnancy safer.
[3]	Central Statistical Agency (CSA) [Ethiopia] and ICF, Ethiopia Demographic and Health Survey 2011: Key Indicators Report. 2012: Addis Ababa, Ethiopia, and Rockville, Maryland, USA, CSA, and ICF.
[4]	Validation of neonatal tetanus elimination in Andhra Pradesh Weekly Epidemiological Record, 2004. 79: p. 292-297.
[5]	Fauveau V et al., Maternal tetanus: magnitude, epidemiology, and potential control measures. International Journal of Gynecology and Obstetrics, 1993. 40: p. 3-12.
[6]	WHO, Standards for maternal and Neonatal care: Integrated management of pregnancy and chield birth. 2007, Department of making pregnancy safer.
[7]	Han, J., M. Kamber, and J. Pei, eds. Data mining concepts and techniques. Third ed. 2013, Morgan Kaufmann Publishers: Waltham, Mass.
[8]	G. Rasitha Banu, A Role of decision Tree classification data Mining Technique in Diagnosing Thyroid disease. International Journal of Computer Sciences and Engineering, 2016. 4 (11).
[9]	Ian H. Witten and Eibe Frank, eds. Data Mining: Practical Machine Learning Tools and Techniques. Second edition. 2005, Morgan Kaufmann publications.
[10]	Parvez Ahmad, Saqib Qamar, and Syed Qasim Afser Rizvi, Techniques of Data Mining In Healthcare: A Review. International Journal of Computer Applications, 2015. 120 (15).
[11]	P. L. Geenen, et al., Constructing naive Bayesian classifiers for veterinary medicine: A case study in the clinical diagnosis of classical swine fever. Research in Veterinary Science, 2010. 91: p. 64-70.
[12]	Yi Peng, et al., Application of Clustering Methods to Health Insurance Fraud Detection. 2006.

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Kedir Hussein Abegaz, Emiru Merdassa Atomssa. (2017). Data Mining of Access to Tetanus Toxoid Immunization Among Women of Childbearing Age in Ethiopia. Machine Learning Research, 2(2), 54-60. https://doi.org/10.11648/j.mlr.20170202.12

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Kedir Hussein Abegaz; Emiru Merdassa Atomssa. Data Mining of Access to Tetanus Toxoid Immunization Among Women of Childbearing Age in Ethiopia. Mach. Learn. Res. 2017, 2(2), 54-60. doi: 10.11648/j.mlr.20170202.12

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Kedir Hussein Abegaz, Emiru Merdassa Atomssa. Data Mining of Access to Tetanus Toxoid Immunization Among Women of Childbearing Age in Ethiopia. Mach Learn Res. 2017;2(2):54-60. doi: 10.11648/j.mlr.20170202.12

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@article{10.11648/j.mlr.20170202.12,
  author = {Kedir Hussein Abegaz and Emiru Merdassa Atomssa},
  title = {Data Mining of Access to Tetanus Toxoid Immunization Among Women of Childbearing Age in Ethiopia},
  journal = {Machine Learning Research},
  volume = {2},
  number = {2},
  pages = {54-60},
  doi = {10.11648/j.mlr.20170202.12},
  url = {https://doi.org/10.11648/j.mlr.20170202.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mlr.20170202.12},
  abstract = {Tetanus toxoid (TT) vaccine is given to women of childbearing age to prevent neonatal tetanus and maternal mortality attributed to tetanus. Globally, tetanus is responsible for 5% of maternal deaths and 14% of neonatal deaths annually. Data mining is the process of discovering interesting patterns and knowledge from large amounts of data. Thus, the aim of this study was to identify the best classifier, and to predict the pattern from the TT data set using the data mining algorithms technique. The data for this study were the Tetanus Toxoid data set from the Ethiopian Demographic and Health Survey (EDHS) 2011, and analyzed using the Knowledge discovery process of Selection, Processing, Transforming, mining, and interpretation. The WEKA 3.6.1 tool was used for classification, clustering, association and attribute selection. The accuracy rate of the classifiers on training data is relatively higher than on test data and the multilayer perceptron is the best classifier in our data set on Tetanus toxoid. In the cross-validation with 10 folds, correctly classified best are by naïve Bayesian 63.30% and the least accurate were by k-nearest neighbor 60.52%. Single data instance test using Naïve Bayesian was done by creating test 1, test 2, test 3, and test 4 data test instance, three of them are correctly predicted but one of them incorrectly classified. The maximum confidence attained in the general association is 0.98. But, in the class attribute, it is 0.72. The literacy status of the mother has high information gain with the value 0.046. As a conclusion, the best algorithm based on the TT vaccination data is multilayer perceptron classifier with an accuracy of 67.28% and the total time taken to build the model is at 0.01 seconds. Multilayer perceptron classifier has the lowest average error at 32.72% compared to others. These results suggest that among the machine learning algorithm tested, multilayer perceptron classifier has the potential to significantly improve the conventional classification methods for use in EDHS data of Tetanus toxoid.},
 year = {2017}
}

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TY - JOUR
T1 - Data Mining of Access to Tetanus Toxoid Immunization Among Women of Childbearing Age in Ethiopia
AU - Kedir Hussein Abegaz
AU - Emiru Merdassa Atomssa
Y1 - 2017/03/09
PY - 2017
N1 - https://doi.org/10.11648/j.mlr.20170202.12
DO - 10.11648/j.mlr.20170202.12
T2 - Machine Learning Research
JF - Machine Learning Research
JO - Machine Learning Research
SP - 54
EP - 60
PB - Science Publishing Group
SN - 2637-5680
UR - https://doi.org/10.11648/j.mlr.20170202.12
AB - Tetanus toxoid (TT) vaccine is given to women of childbearing age to prevent neonatal tetanus and maternal mortality attributed to tetanus. Globally, tetanus is responsible for 5% of maternal deaths and 14% of neonatal deaths annually. Data mining is the process of discovering interesting patterns and knowledge from large amounts of data. Thus, the aim of this study was to identify the best classifier, and to predict the pattern from the TT data set using the data mining algorithms technique. The data for this study were the Tetanus Toxoid data set from the Ethiopian Demographic and Health Survey (EDHS) 2011, and analyzed using the Knowledge discovery process of Selection, Processing, Transforming, mining, and interpretation. The WEKA 3.6.1 tool was used for classification, clustering, association and attribute selection. The accuracy rate of the classifiers on training data is relatively higher than on test data and the multilayer perceptron is the best classifier in our data set on Tetanus toxoid. In the cross-validation with 10 folds, correctly classified best are by naïve Bayesian 63.30% and the least accurate were by k-nearest neighbor 60.52%. Single data instance test using Naïve Bayesian was done by creating test 1, test 2, test 3, and test 4 data test instance, three of them are correctly predicted but one of them incorrectly classified. The maximum confidence attained in the general association is 0.98. But, in the class attribute, it is 0.72. The literacy status of the mother has high information gain with the value 0.046. As a conclusion, the best algorithm based on the TT vaccination data is multilayer perceptron classifier with an accuracy of 67.28% and the total time taken to build the model is at 0.01 seconds. Multilayer perceptron classifier has the lowest average error at 32.72% compared to others. These results suggest that among the machine learning algorithm tested, multilayer perceptron classifier has the potential to significantly improve the conventional classification methods for use in EDHS data of Tetanus toxoid.
VL - 2
IS - 2
ER -

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Author Information

Kedir Hussein Abegaz

Biostatistics and Health Informatics, Public Health Department, College of Health Sciences, Madda Walabu University, Bale Goba, Ethiopia
Emiru Merdassa Atomssa

Biostatistics and Health Informatics, West Wollega Zonal Health Department, Gimbi, Oromia, Ethiopia

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APA Style

Kedir Hussein Abegaz, Emiru Merdassa Atomssa. (2017). Data Mining of Access to Tetanus Toxoid Immunization Among Women of Childbearing Age in Ethiopia. Machine Learning Research, 2(2), 54-60. https://doi.org/10.11648/j.mlr.20170202.12

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ACS Style

Kedir Hussein Abegaz; Emiru Merdassa Atomssa. Data Mining of Access to Tetanus Toxoid Immunization Among Women of Childbearing Age in Ethiopia. Mach. Learn. Res. 2017, 2(2), 54-60. doi: 10.11648/j.mlr.20170202.12

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AMA Style

Kedir Hussein Abegaz, Emiru Merdassa Atomssa. Data Mining of Access to Tetanus Toxoid Immunization Among Women of Childbearing Age in Ethiopia. Mach Learn Res. 2017;2(2):54-60. doi: 10.11648/j.mlr.20170202.12

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@article{10.11648/j.mlr.20170202.12,
  author = {Kedir Hussein Abegaz and Emiru Merdassa Atomssa},
  title = {Data Mining of Access to Tetanus Toxoid Immunization Among Women of Childbearing Age in Ethiopia},
  journal = {Machine Learning Research},
  volume = {2},
  number = {2},
  pages = {54-60},
  doi = {10.11648/j.mlr.20170202.12},
  url = {https://doi.org/10.11648/j.mlr.20170202.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mlr.20170202.12},
  abstract = {Tetanus toxoid (TT) vaccine is given to women of childbearing age to prevent neonatal tetanus and maternal mortality attributed to tetanus. Globally, tetanus is responsible for 5% of maternal deaths and 14% of neonatal deaths annually. Data mining is the process of discovering interesting patterns and knowledge from large amounts of data. Thus, the aim of this study was to identify the best classifier, and to predict the pattern from the TT data set using the data mining algorithms technique. The data for this study were the Tetanus Toxoid data set from the Ethiopian Demographic and Health Survey (EDHS) 2011, and analyzed using the Knowledge discovery process of Selection, Processing, Transforming, mining, and interpretation. The WEKA 3.6.1 tool was used for classification, clustering, association and attribute selection. The accuracy rate of the classifiers on training data is relatively higher than on test data and the multilayer perceptron is the best classifier in our data set on Tetanus toxoid. In the cross-validation with 10 folds, correctly classified best are by naïve Bayesian 63.30% and the least accurate were by k-nearest neighbor 60.52%. Single data instance test using Naïve Bayesian was done by creating test 1, test 2, test 3, and test 4 data test instance, three of them are correctly predicted but one of them incorrectly classified. The maximum confidence attained in the general association is 0.98. But, in the class attribute, it is 0.72. The literacy status of the mother has high information gain with the value 0.046. As a conclusion, the best algorithm based on the TT vaccination data is multilayer perceptron classifier with an accuracy of 67.28% and the total time taken to build the model is at 0.01 seconds. Multilayer perceptron classifier has the lowest average error at 32.72% compared to others. These results suggest that among the machine learning algorithm tested, multilayer perceptron classifier has the potential to significantly improve the conventional classification methods for use in EDHS data of Tetanus toxoid.},
 year = {2017}
}

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