title: Determinants and development of a web-based child mortality prediction model in resource-limited settings: A data mining approach.
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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Health Policy and Planning Directorate, Ethiopian Federal Ministry of Health, Addis Ababa, Ethiopia. Electronic address: balagerusew7@gmail.com.
authors.forename: Brook
authors.initials: B
authors.lastname: Tesfaye
authors.affiliation: Graduate Institute of Biomedical Informatics, Taipei Medical University, Taiwan.
authors.forename: Suleman
authors.initials: S
authors.lastname: Atique
authors.affiliation: Health Policy and Planning Directorate, Ethiopian Federal Ministry of Health, Addis Ababa, Ethiopia.
authors.forename: Noah
authors.initials: N
authors.lastname: Elias
authors.affiliation: Health Information Technology Directorate, Ethiopian Federal Ministry of Health, Addis Ababa, Ethiopia.
authors.forename: Legesse
authors.initials: L
authors.lastname: Dibaba
authors.affiliation: Graduate Institute of Biomedical Informatics, Taipei Medical University, Taiwan.
authors.forename: Syed-Abdul
authors.initials: SA
authors.lastname: Shabbir
authors.affiliation: University of Gondar, College of Medicine and Health Science, Institute of Public Health, Gondar, Ethiopia; Leibniz Institute for Prevention Research and Epidemiology - BIPS, Achterstraße 30, Bremen, Germany; University of Bremen, Health Sciences, Bremen, Germany.
authors.forename: Mihiretu
authors.initials: M
authors.lastname: Kebede
mesh_terms: D002648:Child; D046688:Child Mortality; D057225:Data Mining; D005002:Ethiopia / Q000453:epidemiology; D006801:Humans; D020407:Internet
keywords: Child mortality; Data mining; Developing country; Ethiopia; Sustainable development goals