An assessment of random forest technique using simulation study: illustration with infant mortality in Bangladesh

Rahman, Atikur and Hossain, Zakir and Kabir, Enamul ORCID: https://orcid.org/0000-0002-6157-2753 and Rois, Rumana (2022) An assessment of random forest technique using simulation study: illustration with infant mortality in Bangladesh. Health Information Science and Systems, 10:12. pp. 1-8.


Abstract

We aimed to assess different machine learning techniques for predicting infant mortality (<1 year) in Bangladesh. The decision tree (DT), random forest (RF), support vector machine (SVM) and logistic regression (LR) approaches were evaluated through accuracy, sensitivity, specificity, precision, F1-score, receiver operating characteristics curve and k-fold cross-validation via simulations. The Boruta algorithm and chi-square (χ2) test were used for features selection of infant mortality. Overall, the RF technique (Boruta: accuracy = 0.8890, sensitivity = 0.0480, specificity = 0.9789, precision = 0.1960, F1-score = 0.0771, AUC = 0.6590; χ2: accuracy = 0.8856, sensitivity = 0.0536, specificity = 0.9745, precision = 0.1837, F1-score = 0.0828, AUC = 0.6480) showed higher predictive performance for infant mortality compared to other approaches. Age at first marriage and birth, body mass index (BMI), birth interval, place of residence, religion, administrative division, parents education, occupation of mother, media-exposure, wealth index, gender of child, birth order, children ever born, toilet facility and cooking fuel were potential determinants of infant mortality in Bangladesh. Study findings may help women, stakeholders and policy-makers to take necessary steps for reducing infant mortality by creating awareness, expanding educational programs at community levels and public health interventions.


Statistics for USQ ePrint 49273
Statistics for this ePrint Item
Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Files associated with this item cannot be displayed due to copyright restrictions.
Faculty/School / Institute/Centre: Current – Faculty of Health, Engineering and Sciences - School of Mathematics, Physics and Computing (1 Jan 2022 -)
Faculty/School / Institute/Centre: Current – Faculty of Health, Engineering and Sciences - School of Mathematics, Physics and Computing (1 Jan 2022 -)
Date Deposited: 23 Jun 2022 23:55
Last Modified: 23 Jun 2022 23:55
Uncontrolled Keywords: Machine learning, Random forest, Boruta algorithm, Chi-square, Infant mortality
Fields of Research (2020): 42 HEALTH SCIENCES > 4206 Public health > 420699 Public health not elsewhere classified
Socio-Economic Objectives (2020): 20 HEALTH > 2099 Other health > 209999 Other health not elsewhere classified
Identification Number or DOI: https://doi.org/10.1007/s13755-022-00180-0
URI: http://eprints.usq.edu.au/id/eprint/49273

Actions (login required)

View Item Archive Repository Staff Only