Machine learning algorithm for modelling infant mortality in Bangladesh
Conference or Workshop item
Paper/Presentation Title | Machine learning algorithm for modelling infant mortality in Bangladesh |
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Authors | Rahman, Atikur (Author), Hossain, Zakir (Author), Kabir, Enamul (Author) and Rois, Rumana (Author) |
Editors | Siuly, Siuly, Wang, Hua, Chen, Lu, Guo, Yanhui and Xing, Chunxiao |
Journal or Proceedings Title | Proceedings of the 10th International Conference on Health Information Science (HIS 2021) |
Journal Citation | 13079, pp. 205-219 |
Number of Pages | 15 |
Year | 2021 |
Place of Publication | Switzerland |
ISBN | 9783030908843 |
9783030908850 | |
Digital Object Identifier (DOI) | https://doi.org/10.1007/978-3-030-90885-0_19 |
Web Address (URL) of Paper | https://link.springer.com/chapter/10.1007/978-3-030-90885-0_19 |
Web Address (URL) of Conference Proceedings | https://link.springer.com/book/10.1007/978-3-030-90885-0 |
Conference/Event | 10th International Conference on Health Information Science (HIS 2021) |
Event Details | 10th International Conference on Health Information Science (HIS 2021) Parent International Conference on Health Information Science (HIS) Delivery In person Event Date 25 to end of 28 Oct 2021 Event Location 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. |
Keywords | infant mortality, machine learning, boruta algorithm, random forest, AUC |
ANZSRC Field of Research 2020 | 420699. Public health not elsewhere classified |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Series | Lecture Notes in Computer Science |
Byline Affiliations | Jahangirnagar University, Bangladesh |
University of Dhaka, Bangladesh | |
School of Sciences | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q6x13/machine-learning-algorithm-for-modelling-infant-mortality-in-bangladesh
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