References
- Sow B, Mukhtar H, Ahmad HF, Suguri H. Assessing the relative importance of social determinants of health in malaria and anemia classification based on machine learning techniques. Informatics Heal Soc Care. Internet]. 2019 Mar 27;1–13. Available from:
- Goldstein BA, Navar AM, Carter RE. Moving beyond regression techniques in cardiovascular risk prediction: applying machine learning to address analytic challenges. Eur Heart J. Internet]. 2016 Jul 19;ehw302. Available from:
- Hastie T, Tibshirani R, Friedman J. The elements of statistical learning: data mining, inference, and prediction. Ney York: Springer Science & Business Media; 2009.
- De Onis M, Branca F. Childhood stunting: a global perspective. Matern Child Nutr [Internet]. 2016;12(S1):12–26. May; Available from: .
- NIPORT, Mitra and Associates, ICF International. Bangladesh demographic and health survey 2014, Dhaka, Bangladesh, and Rockville, Maryland, USA: NIPORT, Mitra and Associates, and ICF International; 2016.
- Unicef/WHO/The World Bank. Levels and Trends in Child malnutrition - Unicef WHO The World Bank Joint Child Malnutrition Estimates [Internet]. Unicef. Geneva (Switzerland); 2019. Available from: http://www.unicef.org/media/files/JME_2015_edition_Sept_2015.pdf
- UNICEF. Maximising the growth of children speeding up the work to reduce stunting [Internet]. 2019 [ cited 2020 Jan 20]. Available from: https://www.unicef.org/bangladesh/en/maximising-growth-children
- Rajia S, Sabiruzzaman M, Islam MK, Hossain MG, Lestrel PE. Trends and future of maternal and child health in Bangladesh. PLoS One [Internet]. Uthman O, editor 2017 Mar 15;14(3):e0211875. Available from: .
- Sarma H, Khan JR, Asaduzzaman M, Uddin F, Tarannum S, Hasan MM. et al., Factors influencing the prevalence of stunting among children aged below five years in Bangladesh. Food Nutr Bull [Internet]. 2017 Sep 30; 38(3):291–301. Available from:
- Huda TM, Hayes A, El Arifeen S, Dibley MJ Social determinants of inequalities in child undernutrition in Bangladesh: a decomposition analysis. Matern Child Nutr [Internet]. 2018 Jan;14(1):e12440. Available from: .
- Islam MM, Sanin KI, Mahfuz M, Ahmed AMS, Mondal D, Haque R. et al., Risk factors of stunting among children living in an urban slum of Bangladesh: findings of a prospective cohort study. BMC Public Health [Internet]. 2018 Dec 30; 18(1):197. Available from:
- Chowdhury MRK, Rahman MS, Khan MMH, Mondal MNI, Rahman MM, Billah B. Risk factors for child malnutrition in Bangladesh: a multilevel analysis of a nationwide population-based survey. J Pediatr [Internet] [ cited 2019 Jul 12];172:194–201.e1. Available from: 2016 May1 https://www.sciencedirect.com/science/article/pii/S0022347616000251?via%3Dihub.
- Svefors P, Sysoev O, Ekstrom E-C, Persson LA, Arifeen SE, Naved RT, Rahman A, Khan AI, Selling K. Relative importance of prenatal and postnatal determinants of stunting: data mining approaches to the MINIMat cohort, Bangladesh. BMJ Open [Internet]. 9(8):e025154. Aug 5 2019; Available from: .
- Shahriar MM, Iqubal MS, Mitra S, Das AK. A deep learning approach to predict malnutrition status of 0-59 month’s older children in Bangladesh. In: 2019 IEEE International Conference on Industry 40, Artificial Intelligence, and Communications Technology (IAICT) [Internet]. Bali, Indonesia: IEEE; 2019. p. 145–49. Available from: https://ieeexplore.ieee.org/document/8784823/
- Ahmed AS, Ahmed T, Roy S, Alam N, Hossain MI. Determinants of undernutrition in children under 2 years of age from rural Bangladesh. Indian Pediatr [Internet]. 49(10):821–24. Oct 10 2012; Available from: .
- Stewart CP, Iannotti L, Dewey KG, Michaelsen KF, Onyango AW. Contextualising complementary feeding in a broader framework for stunting prevention. Child Nutr [Internet]. 2013;9:27–45. doi:https://doi.org/10.1111/mcn.12088.
- Mistry SK, Hossain MB, Khanam F, Akter F, Parvez M, Yunus FM. et al., Individual -, maternal- and household-level factors associated with stunting among children aged 0–23 months in Bangladesh. Public Health Nutr [Internet]. 2019 Jan 8;22(1): 85–94. Available from: https://www.cambridge.org/core/product/identifier/S1368980018002926/type/journal_article
- Hossain MB, Khan MHR. Role of parental education in reduction of prevalence of childhood undernutrition in Bangladesh. Public Health Nutr. 2018;21(10):1845–54. doi:https://doi.org/10.1017/S1368980018000162.
- Bhowmik KR, Das S. On selection of an appropriate logistic model to determine the risk factors of childhood stunting in Bangladesh. Matern Child Nutr [Internet]. 2019 Jan 23; 15(1): Available from:
- Das S, Gulshan J. Different forms of malnutrition among under five children in Bangladesh: a cross sectional study on prevalence and determinants. BMC Nutr [Internet]. 2017 Dec 3;3(1): 1. Available from:
- Rah JH, Akhter N, Semba RD, Pee SD, Bloem MW, Campbell AA, Moench-Pfanner R, Sun K, Badham J, Kraemer K. et al., Low dietary diversity is a predictor of child stunting in rural Bangladesh. Eur J Clin Nutr [Internet]. 2010 Dec 15 [ cited 2018 Sep 14];64(12):1393–98. Available from: http://www.ncbi.nlm.nih.gov/pubmed/20842167
- Semba RD, De Pee S, Sun K, Sari M, Akhter N, Bloem MW. Effect of parental formal education on risk of child stunting in Indonesia and Bangladesh: a cross-sectional study. Lancet [Internet]. 2008 Jan;371(9609):322–28. https://linkinghub.elsevier.com/retrieve/pii/S0140673608601695.
- Rahman A, Chowdhury S. Determinants of chronic malnutrition among preschool children in Bangladesh. J Biosoc Sci [Internet]. 39(2):161–73. Mar 28 2007; Available from: https://www.cambridge.org/core/product/identifier/S0021932006001295/type/journal_article.
- Akram R, Sultana M, Ali N, Sheikh N, Sarker AR. Prevalence and determinants of stunting among preschool children and its urban–rural disparities in Bangladesh. Food Nutr Bull [Internet] [ cited 2019 Jul 12];39(4):521–35. Available from: 2018 Dec 29 .
- Tibshirani R Regression shrinkage and selection via the Lasso. J R Stat Soc Ser B [Internet]. 1996 Jan;58(1):267–88. Available from: .
- Lee SI, Lee H, Abbeel P, Ng AY Efficient L 1 regularized logistic regression. In: Proceedings of the National Conference on Artificial Intelligence, Boston, Massachussetts. 2006. p. 401–08.
- Fisher RA. The use of multiple measurements in taxonomic problems. Annals of Eugenics. 1936;7(2):179–88. doi:https://doi.org/10.1111/j.1469-1809.1936.tb02137.x.
- Friedman JH. Regularized discriminant analysis. J Am Stat Assoc [Internet]. 1989 Mar 1;84(405): 165–75. Available from:
- Breiman L, Friedman JH, Olshen RA, Stone CJ. Classification and regression trees. 1st. California (USA): Belmont, Chapman and Hall/CRC; 1984.
- Breiman L. Random forests. Mach Learn. 2001;45(1):5–32. doi:https://doi.org/10.1023/A:1010933404324.
- Mason L, Baxter J, Bartlett P, Frean M. Boosting algorithms as gradient descent. Advances in neural information processing systems. 2000. 512–18.
- Cortes C, Vapnik V. Support-vector networks. Mach Learn. 1995;20(3):273–97. doi:https://doi.org/10.1007/BF00994018.
- Ripley BD. Pattern recognition and neural networks. Cambridge: Cambridge university press; 2007.
- Breiman L. Bagging predictors. Mach Learn. 1996;24(2):123–40. doi:https://doi.org/10.1007/BF00058655.
- Breiman L. Heuristics of instability and stabilization in model selection. Ann Stat. 1996;24(6):2350–83. doi:https://doi.org/10.1214/aos/1032181158.
- Schapire RE. The strength of weak learnability. Mach Learn. 1990;5(2):197–227. doi:https://doi.org/10.1007/BF00116037.
- Freund Y. Boosting a weak learning algorithm by majority. Inf Comput [Internet]. 1995 Sep;121(2):256–85. https://linkinghub.elsevier.com/retrieve/pii/S0890540185711364.
- Freund Y, Schapire RE Experiments with a new boosting algorithm. In: Proceedings of the 13th International Conference on Machine Learning, Bari, Italy [Internet]. 1996. p. 148–56. Available from: http://www.public.asu.edu/~jye02/CLASSES/Fall-2005/PAPERS/boosting-icml.pdf
- Freund Y, Schapire RE Game theory, on-line prediction and boosting. In: Proceedings of the Annual ACM Conference on Computational Learning Theory, Desenzano del Garda, Italy. 1996. p. 325–32.
- Freund Y, Schapire RE. A decision-theoretic generalization of on-line learning and an application to boosting. J Comput Syst Sci [Internet]. 1997 Aug;55(1):119–39. https://linkinghub.elsevier.com/retrieve/pii/S002200009791504X.
- Boser BE, Guyon IM, Vapnik VN A training algorithm for optimal margin classifiers. In: Proceedings of the fifth annual workshop on Computational learning theory - COLT ’92 [Internet]. New York (New York, USA): ACM Press; 1992. p. 144–52. Available from: http://portal.acm.org/citation.cfm?doid=130385.130401
- McCulloch WS, Pitts W. A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys [Internet]. 5(4):115–33. Dec 5 1943; Available from: .
- Widrow B, Hoff ME. Adaptive switching circuits. IRE WESCON Conv Rec. 1960;4:96–104.
- Rosenblatt F. Principles of neurodynamics. perceptrons and the theory of brain mechanisms. Buffalo NY, USA: Cornell Aeronautical Lab Inc; 1961.