Skip to main content
Book cover

International Conference on Health Information Science

HIS 2021: Health Information Science pp 205–219Cite as

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.

This is a preview of subscription content, access via your institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-90885-0_19
  • Chapter length: 15 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   54.99
Price excludes VAT (USA)
  • ISBN: 978-3-030-90885-0
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   69.99
Price excludes VAT (USA)
Learn about institutional subscriptions
Fig. 1.
Fig. 2.
Fig. 3.

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.

References

  1. CDC: Infant Mortality. Centers for Disease Control and Prevention (2018). https://www.cdc.gov/reproductivehealth/MaternalInfantHealth/InfantMortality.htm. Accessed 14 July 2021

  2. World Health Organization (WHO). Millennium development goals (MDGs) (2018). http://www.who.int/topics/millennium-development-goals/about/en. Accessed 14 July 2021

  3. World Health Organization (WHO). The global health observatory (2018). https://www.who.int/data/gho/data/themes/topics/indicator-groups/indicator-group-details/GHO/infant-mortality. Accessed 14 July 2021

  4. Vijay, J., Patel, K.K.: Risk factors of infant mortality in Bangladesh. Clin. Epidemiol. Global Health 8, 211–214 (2020)

    CrossRef  Google Scholar 

  5. Hajizadeh, M., Nandi, A., Heymann, J.: Social inequality in infant mortality: what explains variation across low and middle income countries? Soc. Sci. Med. 101, 36–46 (2014)

    CrossRef  Google Scholar 

  6. World Health Organization (WHO). Success factor for women’s and child’s health: Bangladesh (2015). www.who.int

  7. Quansah, E., Ohene, L.A., Norman, L., Mireku, M.O., Karikari, T.K.: Social factors influencing child health in Ghana. PLoS One 11(1), 1–10 (2016)

    CrossRef  Google Scholar 

  8. Kiross, G.T., Chojenta, C., Barker, D., Tiruye, T.Y., Loxton, D.: The effect of maternal education on infant mortality in Ethiopia: a systematic review and meta-analysis. PLoS One 14(7), e0220076 (2019)

    CrossRef  Google Scholar 

  9. Dube, L., Taha, M., Asefa, H.: Determinants of infant mortality in community of Gilgel gibe field research center, Southwest Ethiopia: a matched case control study. BMC Public Health 13, 401 (2013)

    CrossRef  Google Scholar 

  10. Leal, M.D., Bittencourt, S.D., Torres, R.M., Niquini, R.P., Souza, P.R., Jr.: Determinants of infant mortality in the Jequitinhonha valley and in the north and northeast regions of Brazil. Rev Saude Publica 51(12), 1–9 (2017)

    Google Scholar 

  11. Khadka, K.B., Lieberman, L.S., Giedraitis, V., Bhatta, L., Pandey, G.: The socio-economic determinants of infant mortality in Nepal: analysis of Nepal demographic health survey. BMC Pediatr. 15(152), 1 (2015)

    Google Scholar 

  12. Santos, S.L., Santos, L.B., Campelo, V., Silva, A.R.: Factors associated with infant mortality in a northeastern Brazilian capital. Rev. Bras. Ginecol. Obstet. 38(10), 482–491 (2016)

    CrossRef  Google Scholar 

  13. Baraki, A.G., et al.: Factors affecting infant mortality in the general population: evidence from the 2016 Ethiopian demographic and health survey (EDHS); a multilevel analysis. BMC Pregnancy Childbirth 20, 299 (2020)

    CrossRef  Google Scholar 

  14. Varghese, S., Prasad, J.H., Jacob, K.S.: Domestic violence as a risk factor for infant and child mortality: a community-based case-control study from southern India. Natl. Med. J. India 26(3), 142–146 (2013)

    Google Scholar 

  15. Mohamoud, Y.A., Kirby, R.S., Ehrenthal, D.B.: Poverty, urban-rural classification and term infant mortality: a population-based multilevel analysis. BMC Pregnancy Childbirth 19, 40 (2019)

    CrossRef  Google Scholar 

  16. de Bitencourt, F.H., Schwartz, I.V.D., Vianna, F.S.L.: Infant mortality in Brazil attributable to inborn errors of metabolism associated with sudden death: a time-series study (2002–2014). BMC Pediatr. 19, 52 (2019)

    CrossRef  Google Scholar 

  17. Vilanova, C.S., et al.: The relationship between the different low birth weight strata of newborns with infant mortality and the influence of the main health determinants in the extreme south of Brazil. Popul. Health Metrics 15, 1–10 (2019)

    Google Scholar 

  18. Hajipour, M., et al.: Predictive factors of infant mortality using data mining in Iran. J. Comprehen. Pediatr. 12(1), 1–8 (2021)

    Google Scholar 

  19. Dancer, D., Rammohan, A., Smith, M.D.: Infant mortality and child nutrition in Bangladesh. Health Econ. 17(9), 1015–1035 (2008)

    CrossRef  Google Scholar 

  20. Alghamdi, M., Al-Mallah, M., Keteyian, S., Brawner, C., Ehrman, J., Sakr, S.: Predicting diabetes mellitus using SMOTE and ensemble machine learning approach: the Henry Ford Exercise Testing (FIT) project. PLoS One 12, 1 (2017)

    CrossRef  Google Scholar 

  21. Supriya, S., Siuly, S., Wang, H., Zhang, Y.: Automated epilepsy detection techniques from electroencephalogram signals: a review study. Health Inf. Sci. Syst. 8(1), 1–15 (2020). https://doi.org/10.1007/s13755-020-00129-1

    CrossRef  Google Scholar 

  22. Pandey, Y.Z.D., Yin, X., Wang, H.: Accurate vessel segmentation using maximum entropy incorporating line detection and phase-preserving denoising. Comput. Vision Image Underst. 155, 162–172 (2017)

    CrossRef  Google Scholar 

  23. Sarki, R., Ahmed, K., Wang, H., Zhang, Y.: Image Preprocessing in Classification and Identification of Diabetic Eye Diseases. Data Sci. Eng. 1–17 (2021)

    Google Scholar 

  24. Supriya, S., Siuly, S., Wang, H., Zhang, Y.: EEG sleep stages analysis and classification based on weighed complex network features. IEEE Trans. Emerg. Topics Comput. Intell. 5, 236–246 (2018)

    CrossRef  Google Scholar 

  25. Sarki, R., Ahmed, K., Wang, H., Zhang, Y.: Automated detection of mild and multi-class diabetic eye diseases using deep learning. Health Inf. Sci. Syst. 8(1), 1–9 (2020). https://doi.org/10.1007/s13755-020-00125-5

    CrossRef  Google Scholar 

  26. Mateen, B.A., Liley, J., Denniston, A.K., Holmes, C.C., Vollmer, S.J.: Improving the quality of machine learning in health applications and clinical research. Nat. Mach. Intell. 2(10), 554–556 (2020)

    CrossRef  Google Scholar 

  27. National institute of population research and training (NIPROT), Bangladesh demographic and health survey 2017–2018. Mitra and Associates, Dhaka, Bangladesh and ICF International, Calverton, Maryland, USA (2019)

    Google Scholar 

  28. R Core Team: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing. http://www.R-project.org

  29. Kursa, M.B., Rudnicki, W.R.: Feature selection with the Boruta package. J. Statist. Softw. 36(11), 1–13 (2010)

    CrossRef  Google Scholar 

  30. Igual, L., Seguí, S.: Introduction to Data Science. Springer, Cham (2017)

    CrossRef  Google Scholar 

  31. Nilsson, N.L.: Introduction to Machine Learning. Stanford University, Stanford, CA (1997)

    Google Scholar 

  32. Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)

    CrossRef  Google Scholar 

  33. Awad, M., Khanna, R.: Efficient Learning Machines. A press, Berkeley, CA (2015)

    CrossRef  Google Scholar 

  34. Burges, C.J.: A tutorial on support vector machines for pattern recognition. Data Mining Knowl. Disc. 2(2), 121–167 (1998)

    CrossRef  Google Scholar 

  35. Müller, K.R., Mika, S., Rätsch, G., Tsuda, K., Schölkopf, B.: An introduction to kernel-based learning algorithms. IEEE Trans. Neural Netw. 12(2), 181–201 (2001)

    CrossRef  Google Scholar 

  36. Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer-Verlag, New York (1995)

    CrossRef  Google Scholar 

  37. Fawcett, T.: Introduction to ROC analysis. Pattern Recogn. Lett. 27, 861–874 (2006)

    CrossRef  Google Scholar 

  38. Koehrsen, W.: An implementation and explanation of the random forest in Python. Towards Data Sci. 31, 1 (2018)

    Google Scholar 

Download references

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.

Funding

There is no funding for this work.

Author information

Authors and Affiliations

  1. Department of Statistics, Jahangirnagar University, Dhaka, Bangladesh

    Atikur Rahman & Rumana Rois

  2. Department of Statistics, University of Dhaka, Dhaka, Bangladesh

    Zakir Hossain

  3. School of Sciences, University of Southern Queensland, Toowoomba, Australia

    Enamul Kabir

Authors
  1. Atikur Rahman
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zakir Hossain
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Enamul Kabir
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rumana Rois
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Atikur Rahman .

Editor information

Editors and Affiliations

  1. Victoria University, Footscray, VIC, Australia

    Siuly Siuly

  2. Victoria University, Footscray, VIC, Australia

    Hua Wang

  3. Swinburne University of Technology, Melbourne, VIC, Australia

    Dr. Lu Chen

  4. University of Illinois at Springfield, Springfield, NY, USA

    Yanhui Guo

  5. Tsinghua University, Beijing, China

    Chunxiao Xing

Ethics declarations

Conflicts of interest

No conflict of interest exits among the authors.

Patient Consent for Publication

Not applicable.

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.

Rights and permissions

Reprints and Permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

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

Download citation

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

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-90884-3

  • Online ISBN: 978-3-030-90885-0

  • eBook Packages: Computer ScienceComputer Science (R0)