Machine learning algorithm for modelling infant mortality in Bangladesh

Rahman, Atikur and Hossain, Zakir and Kabir, Enamul ORCID: https://orcid.org/0000-0002-6157-2753 and Rois, Rumana (2021) Machine learning algorithm for modelling infant mortality in Bangladesh. In: 10th International Conference on Health Information Science [HIS 2021], 25 Oct - 28 Oct 2021, Melbourne, Australia.


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.


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: Permanent restricted access to Published version in accordance with the copyright policy of the publisher.
Faculty/School / Institute/Centre: Historic - Faculty of Health, Engineering and Sciences - School of Sciences (6 Sep 2019 - 31 Dec 2021)
Faculty/School / Institute/Centre: Current - Institute for Resilient Regions - Centre for Health Research (1 Apr 2020 -)
Date Deposited: 18 Nov 2021 03:21
Last Modified: 21 Nov 2021 00:57
Uncontrolled Keywords: infant mortality, machine learning, boruta algorithm, random forest, AUC
Fields of Research (2008): 11 Medical and Health Sciences > 1199 Other Medical and Health Sciences > 119999 Medical and Health Sciences not elsewhere classified
Fields of Research (2020): 42 HEALTH SCIENCES > 4206 Public health > 420699 Public health not elsewhere classified
Socio-Economic Objectives (2008): E Expanding Knowledge > 97 Expanding Knowledge > 970111 Expanding Knowledge in the Medical and Health Sciences
Socio-Economic Objectives (2020): 20 HEALTH > 2004 Public health (excl. specific population health) > 200499 Public health (excl. specific population health) not elsewhere classified
Identification Number or DOI: https://doi.org/10.1007/978-3-030-90885-0_19
URI: http://eprints.usq.edu.au/id/eprint/44935

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