An assessment of random forest technique using simulation study: illustration with infant mortality in Bangladesh
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,...
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| Published in | Health information science and systems Vol. 10; no. 1; p. 12 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Cham
Springer International Publishing
21.06.2022
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2047-2501 2047-2501 |
| DOI | 10.1007/s13755-022-00180-0 |
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| 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 (
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. |
|---|---|
| AbstractList | 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 ( $$\chi ^2$$ 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; $$\chi ^2$$ 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. 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.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. 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. 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. |
| ArticleNumber | 12 |
| Author | Kabir, Enamul Hossain, Zakir Rahman, Atikur Rois, Rumana |
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| Cites_doi | 10.1371/journal.pone.0026856 10.1371/journal.pone.0262927 10.1023/A:1010933404324 10.5812/compreped.108575 10.1109/72.914517 10.1136/bmjopen-2020-045506 10.1023/A:1009715923555 10.1371/journal.pone.0145401 10.1007/978-3-319-50017-1 10.1007/s13755-019-0084-2 10.1007/978-3-030-90885-0_19 10.18637/jss.v036.i11 10.1186/s12884-019-2190-1 10.1136/bmjopen-2011-000226 10.1371/journal.pone.0179805 10.1007/978-1-4302-5990-9-1 10.1007/s41019-021-00167-z 10.1186/s12887-015-0468-7 10.1055/s-0036-1584686 10.1186/1471-2458-13-401 10.1007/s13755-020-00129-1 10.3329/bjms.v13i4.20590 10.1038/s42256-020-00239-1 10.1002/hec.1379 10.1016/j.socscimed.2013.11.019 10.1186/s12963-019-0195-7 10.1007/978-1-4757-2440-0 10.1016/j.cegh.2019.07.003 10.1007/s11280-019-00776-9 10.1016/j.patrec.2005.10.010 10.1371/journal.pone.0220076 10.1007/s13755-020-00125-5 10.1186/s12884-020-03002-x 10.1186/s12887-019-1421-y |
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HajipourMTaherpourNFatehHPredictive factors of infant mortality using data mining in IranJ Compr Ped202112110.5812/compreped.108575 FH de Bitencourt (180_CR37) 2019; 19 GT Kiross (180_CR8) 2019; 14 YA Mohamoud (180_CR12) 2019; 19 KR Müller (180_CR29) 2001; 12 R Sarki (180_CR20) 2021; 6 M Hajipour (180_CR36) 2021; 12 JE Finlay (180_CR39) 2011 CS Vilanova (180_CR14) 2019 L Breiman (180_CR26) 2001; 45 M Awad (180_CR27) 2015 S Supriya (180_CR21) 2020; 8 CJ Burges (180_CR28) 1998; 2 T Fawcett (180_CR31) 2006; 27 180_CR33 180_CR32 A Singh (180_CR42) 2011; 6 D Dancer (180_CR15) 2008; 17 180_CR6 L Dube (180_CR13) 2013; 13 KB Khadka (180_CR9) 2015; 15 J He (180_CR22) 2020; 23 L Igual (180_CR24) 2017 MM Rahman (180_CR40) 2021 E Quansah (180_CR7) 2016 J Vijay (180_CR4) 2020; 8 J Du (180_CR19) 2019 BA Mateen (180_CR17) 2020; 2 A Palczewska (180_CR35) 2014 180_CR25 VN Vapnik (180_CR30) 1995 SC Karmaker (180_CR41) 2014 AG Baraki (180_CR11) 2020; 20 R Sarki (180_CR18) 2020; 8 SL Santos (180_CR10) 2016; 38 MB Kursa (180_CR34) 2010; 36 180_CR2 180_CR1 M Alghamdi (180_CR16) 2017; 12 MM Hossain (180_CR38) 2022; 17 M Hajizadeh (180_CR5) 2014; 101 180_CR3 180_CR23 |
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| SubjectTerms | 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 |
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| Title | An assessment of random forest technique using simulation study: illustration with infant mortality in Bangladesh |
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