Predictive Factors of Infant Mortality Using Data Mining in Iran

Abstract

Objectives: Reducing infant mortality in the whole world is one of the millennium development goals.The aim of this study was to determine the factors related to infant mortality using data mining algorithms. Methods: This population-based case-control study was conducted in eight provinces of Iran. A sum of 2,386 mothers (1,076 cases and 1,310 controls) enrolled in this study. Data were extracted from health records of mothers and filled with checklists in health centers. We employed several data mining algorithms such as AdaBoost classifier, Support Vector Machine, Artificial Neural Networks, Random Forests, K-nearest neighborhood, and Naïve Bayes in order to recognize the important predictors of infant death; binary logistic regression model was used to clarify the role of each selected predictor. Results: In this study, 58.7% of infant mortalities occurred in rural areas, that 55.6% of them were boys. Moreover, Naïve Bayes and Random Forest were highly capable of predicting related factors among data mining models. Also, the results showed that events during pregnancy such as dental disorders, high blood pressure, loss of parents, factors related to infants such as low birth weight, and factors related to mothers like consanguineous marriage and gap of pregnancy (< 3 years) were all risk factors while the age of pregnancy (18 - 35 year) and a high degree of education were protective factors. Conclusions: Infant mortality is the consequence of a variety of factors, including factors related to infants themselves and their mothers and events during pregnancy. Owing to the high accuracy and ability of modern modeling compared to traditional modeling, it is recommended to use machine learning tools for indicating risk factors of infant mortality.