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

• Published in: Health information science and systems Vol. 10; no. 1; p. 12
• Main Authors: Rahman, Atikur, Hossain, Zakir, Kabir, Enamul, Rois, Rumana
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 21.06.2022; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Bioinformatics; Body size; Chi-square test; Children; Computational Biology/Bioinformatics; Computer Science; Cooking; Decision trees; Health Informatics; Infant mortality; Information Systems and Communication Service; Machine learning; Performance prediction; Public health; Sensitivity analysis; Statistical tests; Support vector machines; Bangladesh; Random forest; Infant mortality; Chi-square; Boruta algorithm; Machine learning
• ISSN: 2047-2501; 2047-2501
• DOI: 10.1007/s13755-022-00180-0

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 ( χ 2 ) 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; χ 2 : 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.