{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T18:34:06Z","timestamp":1784745246694,"version":"3.55.0"},"reference-count":39,"publisher":"Public Library of Science (PLoS)","issue":"7","license":[{"start":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T00:00:00Z","timestamp":1784678400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>Childhood malnutrition remains a major public health concern in Nepal and other low-resource settings, while conventional case-finding approaches are labor-intensive and frequently unavailable in remote areas. This study provides one of the first applications of machine learning and deep learning to identify child malnutrition in Nepal. We systematically compared 16 algorithms spanning deep learning, gradient boosting, and traditional machine learning families, using data from the Nepal Multiple Indicator Cluster Survey (MICS) 2019. A composite malnutrition indicator was constructed by integrating stunting, wasting, and underweight status, and model performance was evaluated using ten metrics, with emphasis on F1-score and recall to account for substantial class imbalance and the high cost of failing to detect malnourished children. Among all models, TabNet achieved the highest scores among evaluated models, likely attributable to its attention-based architecture. A consensus feature importance analysis identified maternal education, household wealth index, and child age as the primary predictors of malnutrition, followed by geographic characteristics, vaccination status, and meal frequency. Collectively, these results demonstrate a scalable, survey-based screening framework for identifying children at elevated risk of malnutrition and for guiding targeted nutritional interventions. The proposed approach supports Nepal\u2019s progress toward the Sustainable Development Goals and offers a transferable methodological template for similar low-resource settings globally.<\/jats:p>","DOI":"10.1371\/journal.pone.0353969","type":"journal-article","created":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T17:47:32Z","timestamp":1784742452000},"page":"e0353969","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep learning approaches show promise for predicting childhood malnutrition: A comparative study with traditional machine learning methods using survey data"],"prefix":"10.1371","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-4049-4772","authenticated-orcid":true,"given":"Deepak","family":"Bastola","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,7,22]]},"reference":[{"issue":"8","key":"pone.0353969.ref001","article-title":"The immune system in children with malnutrition--a systematic review","volume":"9","author":"MJH Rytter","year":"2014","journal-title":"PLoS One"},{"issue":"1","key":"pone.0353969.ref002","article-title":"Early and long-term consequences of nutritional stunting: from childhood to adulthood","volume":"92","author":"V De Sanctis","year":"2021","journal-title":"Acta Biomed"},{"issue":"9609","key":"pone.0353969.ref003","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1016\/S0140-6736(07)61692-4","article-title":"Maternal and child undernutrition: consequences for adult health and human capital","volume":"371","author":"CG Victora","year":"2008","journal-title":"Lancet"},{"issue":"9890","key":"pone.0353969.ref004","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1016\/S0140-6736(13)60937-X","article-title":"Maternal and child undernutrition and overweight in low-income and middle-income countries","volume":"382","author":"RE Black","year":"2013","journal-title":"Lancet"},{"key":"pone.0353969.ref005","unstructured":"Ritchie H. Half of all child deaths are linked to malnutrition. Our World in Data. 2024. https:\/\/ourworldindata.org\/half-child-deaths-linked-malnutrition"},{"key":"pone.0353969.ref006","unstructured":"UNICEF, WHO and The World Bank. Joint Child Malnutrition Estimates (JME) \u2014 Levels and Trends \u2013 2025 edition. 2025. [cited 28 Jul 2025]. https:\/\/data.unicef.org\/resources\/jme\/"},{"issue":"8","key":"pone.0353969.ref007","doi-asserted-by":"crossref","DOI":"10.1136\/bmjopen-2017-017084","article-title":"Determinants of severe acute malnutrition among children under 5 years of age in Nepal: a community-based case-control study","volume":"7","author":"NK Pravana","year":"2017","journal-title":"BMJ Open"},{"key":"pone.0353969.ref008","doi-asserted-by":"crossref","first-page":"101571","DOI":"10.1016\/j.cegh.2024.101571","article-title":"Malnutrition among under-five children in Nepal: a focus on socioeconomic status and maternal BMI","volume":"27","author":"J Vijay","year":"2024","journal-title":"Clin Epidemiol Glob Health"},{"key":"pone.0353969.ref009","article-title":"Prevalence and determinants of stunting among children under 5 years in remote Nepal","volume":"4","author":"A Karki","year":"2023","journal-title":"Res Feedback Clin Care"},{"issue":"10","key":"pone.0353969.ref010","first-page":"1638","article-title":"Machine learning algorithms for predicting stunting among under-five children in Papua New Guinea","volume":"10","author":"H Shen","year":"2023","journal-title":"Children (Basel)"},{"issue":"2","key":"pone.0353969.ref011","first-page":"269","article-title":"Machine learning algorithms for predicting undernutrition among under-five children in Ethiopia","volume":"25","author":"FH Bitew","year":"2022","journal-title":"Public Health Nutr"},{"key":"pone.0353969.ref012","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/DELCON54057.2022.9753080","article-title":"Efficient Machine Learning for Malnutrition Prediction among under-five children in India.","volume-title":"2022 IEEE Delhi Section Conference (DELCON)","author":"S Jain","year":"2022"},{"key":"pone.0353969.ref013","unstructured":"Central Bureau of Statistics (CBS) U. Nepal Multiple Indicator Cluster Survey 2019: Survey Findings Report. Kathmandu, Nepal: Central Bureau of Statistics and UNICEF Nepal; 2020. [cited 26 Jul 2025]. Available from: https:\/\/mics.unicef.org\/surveys"},{"key":"pone.0353969.ref014","volume-title":"WHO Child Growth Standards: Technical Report","author":"World Health Organization","year":"2006"},{"key":"pone.0353969.ref015","doi-asserted-by":"crossref","first-page":"110861","DOI":"10.1016\/j.nut.2020.110861","article-title":"Machine learning algorithms for predicting malnutrition among under-five children in Bangladesh","volume":"78","author":"A Talukder","year":"2020","journal-title":"Nutrition"},{"issue":"1","key":"pone.0353969.ref016","doi-asserted-by":"crossref","first-page":"14336","DOI":"10.1038\/s41598-025-99288-y","article-title":"Identifying determinants of malnutrition in under-five children in Bangladesh: insights from the BDHS-2022 cross-sectional study","volume":"15","author":"T Tamanna","year":"2025","journal-title":"Sci Rep"},{"key":"pone.0353969.ref017","unstructured":"UNICEF. UNICEF Conceptual Framework on Maternal and Child Nutrition. 2020. [cited 7 Feb 2026]. https:\/\/www.unicef.org\/media\/113291\/file\/UNICEFConceptualFramework.pdf"},{"key":"pone.0353969.ref018","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: synthetic minority over-sampling technique","volume":"16","author":"NV Chawla","year":"2002","journal-title":"J Artif Intell Res"},{"issue":"1","key":"pone.0353969.ref019","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1109\/TIT.1967.1053964","article-title":"Nearest neighbor pattern classification","volume":"13","author":"T Cover","year":"1967","journal-title":"IEEE Trans Inform Theory"},{"key":"pone.0353969.ref020","doi-asserted-by":"crossref","DOI":"10.1201\/9781315139470","volume-title":"Classification and regression trees","author":"L Breiman","year":"2017"},{"issue":"1","key":"pone.0353969.ref021","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"L Breiman","year":"2001","journal-title":"Mach Learn"},{"issue":"1","key":"pone.0353969.ref022","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/s10994-006-6226-1","article-title":"Extremely randomized trees","volume":"63","author":"P Geurts","year":"2006","journal-title":"Mach Learn"},{"issue":"2","key":"pone.0353969.ref023","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1111\/j.1469-1809.1936.tb02137.x","article-title":"The use of multiple measurements in taxonomic problems","volume":"7","author":"RA Fisher","year":"1936","journal-title":"Ann Eugenics"},{"issue":"3","key":"pone.0353969.ref024","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1023\/A:1022627411411","article-title":"Support-vector networks","volume":"20","author":"C Cortes","year":"1995","journal-title":"Mach Learn"},{"issue":"3","key":"pone.0353969.ref025","doi-asserted-by":"crossref","first-page":"1937","DOI":"10.1007\/s10462-020-09896-5","article-title":"A comparative analysis of gradient boosting algorithms","volume":"54","author":"C Bent\u00e9jac","year":"2020","journal-title":"Artif Intell Rev"},{"key":"pone.0353969.ref026","article-title":"Lightgbm: A highly efficient gradient boosting decision tree","volume":"30","author":"G Ke","year":"2017","journal-title":"Adv Neural Inform Process Syst"},{"key":"pone.0353969.ref027","article-title":"CatBoost: unbiased boosting with categorical features","volume":"31","author":"L Prokhorenkova","year":"2018","journal-title":"Adv Neural Inform Process Syst"},{"issue":"1","key":"pone.0353969.ref028","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1006\/jcss.1997.1504","article-title":"A decision-theoretic generalization of on-line learning and an application to boosting","volume":"55","author":"Y Freund","year":"1997","journal-title":"J Comput Syst Sci"},{"key":"pone.0353969.ref029","volume-title":"Deep learning","author":"I Goodfellow","year":"2016"},{"key":"pone.0353969.ref030","first-page":"18932","article-title":"Revisiting deep learning models for tabular data","volume":"34","author":"Y Gorishniy","year":"2021","journal-title":"Adv Neural Inform Process Syst"},{"key":"pone.0353969.ref031","doi-asserted-by":"crossref","unstructured":"Cheng HT, Koc L, Harmsen J, Shaked T, Chandra T, Aradhye H, et al. Wide & deep learning for recommender systems. In: Proceedings of the 1st workshop on deep learning for recommender systems. 2016. pp. 7\u201310.","DOI":"10.1145\/2988450.2988454"},{"issue":"8","key":"pone.0353969.ref032","first-page":"6679","article-title":"TabNet: attentive interpretable tabular learning","volume":"35","author":"S\u00d6 Arik","year":"2021","journal-title":"Proc AAAI Conf Artif Intell"},{"issue":"1","key":"pone.0353969.ref033","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1186\/s12911-021-01652-1","article-title":"A machine learning classifier approach for identifying the determinants of under-five child undernutrition in Ethiopian administrative zones","volume":"21","author":"HM Fenta","year":"2021","journal-title":"BMC Med Inform Decis Mak"},{"key":"pone.0353969.ref034","doi-asserted-by":"crossref","first-page":"111571","DOI":"10.1016\/j.nut.2021.111571","article-title":"Predicting undernutrition among elementary schoolchildren in the Philippines using machine learning algorithms","volume":"96","author":"VT Siy Van","year":"2022","journal-title":"Nutrition"},{"key":"pone.0353969.ref035","doi-asserted-by":"crossref","first-page":"112631","DOI":"10.1016\/j.nut.2024.112631","article-title":"Unlocking insights: Using machine learning to identify wasting and risk factors in Egyptian children under 5","volume":"131","author":"A Hendy","year":"2025","journal-title":"Nutrition"},{"issue":"12","key":"pone.0353969.ref036","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0315393","article-title":"Prediction of undernutrition and identification of its influencing predictors among under-five children in Bangladesh using explainable machine learning algorithms","volume":"19","author":"MM Islam","year":"2024","journal-title":"PLoS One"},{"issue":"6","key":"pone.0353969.ref037","article-title":"Investigate the risk factors of stunting, wasting, and underweight among under-five Bangladeshi children and its prediction based on machine learning approach","volume":"16","author":"SMJ Rahman","year":"2021","journal-title":"PLoS One"},{"key":"pone.0353969.ref038","first-page":"9","article-title":"Population Classification upon Dietary Data Using Machine Learning Techniques with IoT and Big Data","volume-title":"Social Network Forensics, Cyber Security, and Machine Learning","author":"JJS Mani","year":"2018"},{"issue":"4","key":"pone.0353969.ref039","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0231866","article-title":"geoBoundaries: a global database of political administrative boundaries","volume":"15","author":"D Runfola","year":"2020","journal-title":"PLoS One"}],"container-title":["PLOS One"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0353969","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T17:47:41Z","timestamp":1784742461000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0353969"}},"subtitle":[],"editor":[{"given":"Merve","family":"Ka\u015f\u0131kc\u0131","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2026,7,22]]},"references-count":39,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2026,7,22]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0353969","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,22]]}}}