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Machine Learning Algorithm for Analysing Infant Mortality in Bangladesh

Part of the Lecture Notes in Computer Science book series (LNISA,volume 13079)

Abstract

The study aims to investigate the potential predictors associated with infant mortality in Bangladesh through machine learning (ML) algorithm. Data on infant mortality of 26145 children were extracted from the latest Bangladesh Demographic and Health Survey 2017–18. The Boruta algorithm was used to extract important features of infant mortality. We adapted decision tree, random forest, support vector machine and logistic regression approaches to explore predictors of infant mortality. Performances of these techniques were evaluated via parameters of confusion matrix and receiver operating characteristics curve. The proportion of infant mortality was 9.7% (2523 out of 26145). Age at first marriage, age at first birth, birth interval, place of residence, administrative division, religion, education of parents, body mass index, gender of child, children ever born, exposure of media, wealth index, birth order, occupation of mother, toilet facility and cooking fuel were selected as significant features of predicting infant mortality. Overall, the random forest (accuracy = 0.893, precision = 0.715, sensitivity = 0.339, specificity = 0.979, F1-score = 0.460, area under the curve: AUC = 0.6613) perfectly and authentically predicted the infant mortality compared with other ML techniques, including individual and interaction effects of predictors. The significant predictors may help the policy-makers, stakeholders and mothers to take initiatives against infant mortality by improving awareness, community-based educational programs and public health interventions.

Keywords

  • Machine learning
  • boruta algorithm
  • random forest
  • auc.

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Data Availability Statement

We used secondary data from the Demographic and Health Surveys (DHS) Program. The data are available online at https://dhsprogram.com/data/available-datasets.cfm.

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Acknowledgements

The authors are thankful to the authority of Bangladesh Demographic and Health Survey (BDHS) for making their data available for free. Authors would also like to express their gratitude to Department of Statistics, Jahangirnagar University, Savar, Dhaka, Bangladesh; Department of Statistics, University of Dhaka, Bangladesh; and Faculty of Health, Engineering and Sciences (HES) of University of Southern Queensland, Australia for the technical support.

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Correspondence to Atikur Rahman .

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Ethics Statement

This article does not include any data of human participants conducted by any of the authors. The Bangladesh Demographic and Health Survey (BDHS) was approved by ICF Macro Institutional Review Board and the National Research Ethics Committee of the Bangladesh Medical Research Council. Written consent was given by participants in relation to this survey before the interview. All identification of the survey participants was dis-identified before publishing the data. In this study, we used the secondary data that are freely available on the DHS website: https://dhsprogram.com/data/available-datasets.cfm.

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Rahman, A., Hossain, Z., Kabir, E., Rois, R. (2021). Machine Learning Algorithm for Analysing Infant Mortality in Bangladesh. In: Siuly, S., Wang, H., Chen, L., Guo, Y., Xing, C. (eds) Health Information Science. HIS 2021. Lecture Notes in Computer Science(), vol 13079. Springer, Cham. https://doi.org/10.1007/978-3-030-90885-0_19

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  • DOI: https://doi.org/10.1007/978-3-030-90885-0_19

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