Determinants and development of a web-based child mortality prediction model in resource-limited settings

A data mining approach

Brook Tesfaye, Suleman Atique, Noah Elias, Legesse Dibaba, Syed Abdul Shabbir, Mihiretu Kebede

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

Background Improving child health and reducing child mortality rate are key health priorities in developing countries. This study aimed to identify determinant sand develop, a web-based child mortality prediction model in Ethiopian local language using classification data mining algorithm. Methods Decision tree (using J48 algorithm) and rule induction (using PART algorithm) techniques were applied on 11,654 records of Ethiopian demographic and health survey data. Waikato Environment for Knowledge Analysis (WEKA) for windows version 3.6.8 was used to develop optimal models. 8157 (70%) records were randomly allocated to training group for model building while; the remaining 3496 (30%) records were allocated as the test group for model validation. The validation of the model was assessed using accuracy, sensitivity, specificity and area under Receiver Operating Characteristics (ROC) curve. Using Statistical Package for Social Sciences (SPSS) version 20.0; logistic regressions and Odds Ratio (OR) with 95% Confidence Interval (CI) was used to identify determinants of child mortality. Results The child mortality rate was 72 deaths per 1000 live births. Breast-feeding (AOR = 1.46, (95% CI [1.22. 1.75]), maternal education (AOR = 1.40, 95% CI [1.11, 1.81]), family planning (AOR = 1.21, [1.08, 1.43]), preceding birth interval (AOR = 4.90, [2.94, 8.15]), presence of diarrhea (AOR = 1.54, 95% CI [1.32, 1.66]), father's education (AOR = 1.4, 95% CI [1.04, 1.78]), low birth weight (AOR = 1.2, 95% CI [0.98, 1.51]) and, age of the mother at first birth (AOR = 1.42, [1.01–1.89]) were found to be determinants for child mortality. The J48 model had better performance, accuracy (94.3%), sensitivity (93.8%), specificity (94.3%), Positive Predictive Value (PPV) (92.2%), Negative Predictive Value (NPV) (94.5%) and, the area under ROC (94.8%). Subsequent to developing an optimal prediction model, we relied on this model to develop a web-based application system for child mortality prediction. Conclusion In this study, nearly accurate results were obtained by employing decision tree and rule induction techniques. Determinants are identified and a web-based child mortality prediction model in Ethiopian local language is developed. Thus, the result obtained could support child health intervention programs in Ethiopia where trained human resource for health is limited. Advanced classification algorithms need to be tested to come up with optimal models.

Original languageEnglish
Pages (from-to)45-51
Number of pages7
JournalComputer Methods and Programs in Biomedicine
Volume140
DOIs
Publication statusPublished - Mar 1 2017

Fingerprint

Child Mortality
Data Mining
Data mining
Confidence Intervals
Decision Trees
Health
ROC Curve
Language
Mothers
Decision trees
Birth Intervals
Education
Health Priorities
Sensitivity and Specificity
Birth Order
Ethiopia
Mortality
Social Sciences
Health Resources
Family Planning Services

Keywords

  • Child mortality
  • Data mining
  • Developing country
  • Ethiopia
  • Sustainable development goals

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Health Informatics

Cite this

Determinants and development of a web-based child mortality prediction model in resource-limited settings : A data mining approach. / Tesfaye, Brook; Atique, Suleman; Elias, Noah; Dibaba, Legesse; Shabbir, Syed Abdul; Kebede, Mihiretu.

In: Computer Methods and Programs in Biomedicine, Vol. 140, 01.03.2017, p. 45-51.

Research output: Contribution to journalArticle

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abstract = "Background Improving child health and reducing child mortality rate are key health priorities in developing countries. This study aimed to identify determinant sand develop, a web-based child mortality prediction model in Ethiopian local language using classification data mining algorithm. Methods Decision tree (using J48 algorithm) and rule induction (using PART algorithm) techniques were applied on 11,654 records of Ethiopian demographic and health survey data. Waikato Environment for Knowledge Analysis (WEKA) for windows version 3.6.8 was used to develop optimal models. 8157 (70{\%}) records were randomly allocated to training group for model building while; the remaining 3496 (30{\%}) records were allocated as the test group for model validation. The validation of the model was assessed using accuracy, sensitivity, specificity and area under Receiver Operating Characteristics (ROC) curve. Using Statistical Package for Social Sciences (SPSS) version 20.0; logistic regressions and Odds Ratio (OR) with 95{\%} Confidence Interval (CI) was used to identify determinants of child mortality. Results The child mortality rate was 72 deaths per 1000 live births. Breast-feeding (AOR = 1.46, (95{\%} CI [1.22. 1.75]), maternal education (AOR = 1.40, 95{\%} CI [1.11, 1.81]), family planning (AOR = 1.21, [1.08, 1.43]), preceding birth interval (AOR = 4.90, [2.94, 8.15]), presence of diarrhea (AOR = 1.54, 95{\%} CI [1.32, 1.66]), father's education (AOR = 1.4, 95{\%} CI [1.04, 1.78]), low birth weight (AOR = 1.2, 95{\%} CI [0.98, 1.51]) and, age of the mother at first birth (AOR = 1.42, [1.01–1.89]) were found to be determinants for child mortality. The J48 model had better performance, accuracy (94.3{\%}), sensitivity (93.8{\%}), specificity (94.3{\%}), Positive Predictive Value (PPV) (92.2{\%}), Negative Predictive Value (NPV) (94.5{\%}) and, the area under ROC (94.8{\%}). Subsequent to developing an optimal prediction model, we relied on this model to develop a web-based application system for child mortality prediction. Conclusion In this study, nearly accurate results were obtained by employing decision tree and rule induction techniques. Determinants are identified and a web-based child mortality prediction model in Ethiopian local language is developed. Thus, the result obtained could support child health intervention programs in Ethiopia where trained human resource for health is limited. Advanced classification algorithms need to be tested to come up with optimal models.",
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AU - Shabbir, Syed Abdul

AU - Kebede, Mihiretu

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N2 - Background Improving child health and reducing child mortality rate are key health priorities in developing countries. This study aimed to identify determinant sand develop, a web-based child mortality prediction model in Ethiopian local language using classification data mining algorithm. Methods Decision tree (using J48 algorithm) and rule induction (using PART algorithm) techniques were applied on 11,654 records of Ethiopian demographic and health survey data. Waikato Environment for Knowledge Analysis (WEKA) for windows version 3.6.8 was used to develop optimal models. 8157 (70%) records were randomly allocated to training group for model building while; the remaining 3496 (30%) records were allocated as the test group for model validation. The validation of the model was assessed using accuracy, sensitivity, specificity and area under Receiver Operating Characteristics (ROC) curve. Using Statistical Package for Social Sciences (SPSS) version 20.0; logistic regressions and Odds Ratio (OR) with 95% Confidence Interval (CI) was used to identify determinants of child mortality. Results The child mortality rate was 72 deaths per 1000 live births. Breast-feeding (AOR = 1.46, (95% CI [1.22. 1.75]), maternal education (AOR = 1.40, 95% CI [1.11, 1.81]), family planning (AOR = 1.21, [1.08, 1.43]), preceding birth interval (AOR = 4.90, [2.94, 8.15]), presence of diarrhea (AOR = 1.54, 95% CI [1.32, 1.66]), father's education (AOR = 1.4, 95% CI [1.04, 1.78]), low birth weight (AOR = 1.2, 95% CI [0.98, 1.51]) and, age of the mother at first birth (AOR = 1.42, [1.01–1.89]) were found to be determinants for child mortality. The J48 model had better performance, accuracy (94.3%), sensitivity (93.8%), specificity (94.3%), Positive Predictive Value (PPV) (92.2%), Negative Predictive Value (NPV) (94.5%) and, the area under ROC (94.8%). Subsequent to developing an optimal prediction model, we relied on this model to develop a web-based application system for child mortality prediction. Conclusion In this study, nearly accurate results were obtained by employing decision tree and rule induction techniques. Determinants are identified and a web-based child mortality prediction model in Ethiopian local language is developed. Thus, the result obtained could support child health intervention programs in Ethiopia where trained human resource for health is limited. Advanced classification algorithms need to be tested to come up with optimal models.

AB - Background Improving child health and reducing child mortality rate are key health priorities in developing countries. This study aimed to identify determinant sand develop, a web-based child mortality prediction model in Ethiopian local language using classification data mining algorithm. Methods Decision tree (using J48 algorithm) and rule induction (using PART algorithm) techniques were applied on 11,654 records of Ethiopian demographic and health survey data. Waikato Environment for Knowledge Analysis (WEKA) for windows version 3.6.8 was used to develop optimal models. 8157 (70%) records were randomly allocated to training group for model building while; the remaining 3496 (30%) records were allocated as the test group for model validation. The validation of the model was assessed using accuracy, sensitivity, specificity and area under Receiver Operating Characteristics (ROC) curve. Using Statistical Package for Social Sciences (SPSS) version 20.0; logistic regressions and Odds Ratio (OR) with 95% Confidence Interval (CI) was used to identify determinants of child mortality. Results The child mortality rate was 72 deaths per 1000 live births. Breast-feeding (AOR = 1.46, (95% CI [1.22. 1.75]), maternal education (AOR = 1.40, 95% CI [1.11, 1.81]), family planning (AOR = 1.21, [1.08, 1.43]), preceding birth interval (AOR = 4.90, [2.94, 8.15]), presence of diarrhea (AOR = 1.54, 95% CI [1.32, 1.66]), father's education (AOR = 1.4, 95% CI [1.04, 1.78]), low birth weight (AOR = 1.2, 95% CI [0.98, 1.51]) and, age of the mother at first birth (AOR = 1.42, [1.01–1.89]) were found to be determinants for child mortality. The J48 model had better performance, accuracy (94.3%), sensitivity (93.8%), specificity (94.3%), Positive Predictive Value (PPV) (92.2%), Negative Predictive Value (NPV) (94.5%) and, the area under ROC (94.8%). Subsequent to developing an optimal prediction model, we relied on this model to develop a web-based application system for child mortality prediction. Conclusion In this study, nearly accurate results were obtained by employing decision tree and rule induction techniques. Determinants are identified and a web-based child mortality prediction model in Ethiopian local language is developed. Thus, the result obtained could support child health intervention programs in Ethiopia where trained human resource for health is limited. Advanced classification algorithms need to be tested to come up with optimal models.

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KW - Sustainable development goals

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