Computer Methods and Programs in Biomedicine 140 (2017) 45–51 Contents lists available at ScienceDirect Computer Methods and Programs in Biomedicine journal homepage: www.elsevier.com/locate/cmpb Determinants and development of a web-based child mortality prediction model in resource-limited settings: A data mining approach Brook Tesfaye a,∗ , Suleman Atique b , Noah Elias a , Legesse Dibaba c , Syed-Abdul Shabbir b , Mihiretu Kebede d,e,f a Health Policy and Planning Directorate, Ethiopian Federal Ministry of Health, Addis Ababa, Ethiopia b Graduate Institute of Biomedical Informatics, Taipei Medical University, Taiwan c Health Information Technology Directorate, Ethiopian Federal Ministry of Health, Addis Ababa, Ethiopia d University of Gondar, College of Medicine and Health Science, Institute of Public Health, Gondar, Ethiopia e Leibniz Institute for Prevention Research and Epidemiology – BIPS, Achterstraße 30, Bremen, Germany f University of Bremen, Health Sciences, Bremen, Germany a r t i c l e i n f o Article history: Received 15 November 2016 Revised 24 November 2016 Accepted 25 November 2016 Keywords: Child mortality Data mining Sustainable development goals Developing country Ethiopia a b s t r a c t Background: Improving child health and reducing child mortality rate are key health priorities in de- veloping 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. Us- ing 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 algo- rithms need to be tested to come up with optimal models. © 2016 Elsevier Ireland Ltd. All rights reserved. 1. Introduction Child mortality is a core indicator for child health and well- being. In 2000, world leaders agreed on the Millennium Devel- opment Goals (MDGs) and called for reducing the Child mortal- ∗ Corresponding author. E-mail address: balagerusew7@gmail.com (B. Tesfaye). ity rate by two thirds between 1990 and 2015. This particular goal is known as the MDG 4 [1]. It refers to the death of infants and children under the age of five or between the ages of one month to four years. The global Child mortality rate dropped 53(50, 55)%, from 91 (89, 92) deaths per 1000 live births in 1990 to 43 (41, 46) in 2015. Over the same period, the annual number of under-five deaths dropped from 12.7 million to 5.9 million [1]. The world as a whole has achieved an accelerating progress in reducing the child mortality rate. Promisingly, sub-Saharan Africa, http://dx.doi.org/10.1016/j.cmpb.2016.11.013 0169-2607/© 2016 Elsevier Ireland Ltd. All rights reserved.