TY - JOUR
T1 - Determinants and development of a web-based child mortality prediction model in resource-limited settings
T2 - A data mining approach
AU - Tesfaye, Brook
AU - Atique, Suleman
AU - Elias, Noah
AU - Dibaba, Legesse
AU - Shabbir, Syed Abdul
AU - Kebede, Mihiretu
N1 - Publisher Copyright:
© 2016 Elsevier Ireland Ltd
PY - 2017/3/1
Y1 - 2017/3/1
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.
KW - Child mortality
KW - Data mining
KW - Developing country
KW - Ethiopia
KW - Sustainable development goals
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U2 - 10.1016/j.cmpb.2016.11.013
DO - 10.1016/j.cmpb.2016.11.013
M3 - Article
C2 - 28254089
AN - SCOPUS:85000580214
SN - 0169-2607
VL - 140
SP - 45
EP - 51
JO - Computer Methods and Programs in Biomedicine
JF - Computer Methods and Programs in Biomedicine
ER -