{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T17:07:58Z","timestamp":1785604078955,"version":"3.56.0"},"reference-count":32,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2025,5,13]],"date-time":"2025-05-13T00:00:00Z","timestamp":1747094400000},"content-version":"vor","delay-in-days":12,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Brain and Behavior"],"published-print":{"date-parts":[[2025,5]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>Malnutrition remains a critical public health issue in low\u2010income countries, significantly hindering economic development and contributing to over 50% of infant deaths. Under nutrition weakens immune systems, increasing susceptibility to common illnesses and prolonging recovery periods. This study aims to develop and evaluate a novel artificial intelligence\u2010based classification method for nutritional status assessment using hybrid machine learning strategies, enhancing the accuracy and reliability of malnutrition detection.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Method<\/jats:title>\n                    <jats:p>This study utilizes the fire hawk optimizer\u2010based k\u2010means (FHO\u2010K\u2010Means) clustering method to identify key physiological indicators associated with under nutrition in children. The analysis is conducted using the UNICEF dataset, encompassing data from Afghanistan, Albania, Algeria, and Zimbabwe. Following data normalization, the dataset is clustered via FHO\u2010K\u2010Means to establish optimal groupings. The clustered data is then partitioned into training and testing sets for classification using the extreme gradient boosting fuzzy (EGBF). The EGBF model is employed to classify nutritional states, including stunting, wasting, severe wasting, overweight, and underweight, providing a robust framework for predictive analysis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Findings<\/jats:title>\n                    <jats:p>The proposed FHO\u2010K\u2010Means and EGBF model demonstrated superior performance, achieving 99.84% accuracy, 99.5% precision, 99.8% specificity, and 100% sensitivity, with an F1 measure of 98.6% and a mean squared error (MSE) of 0.01%, outperforming existing classification techniques. These results indicate that the model offers a highly effective and scalable tool for identifying at\u2010risk populations, informing targeted interventions to reduce the prevalence of childhood malnutrition, and lowering morbidity in poor countries.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This study developed an innovative FHO\u2010K mean clustering and EGBF classification method for assessing childhood nutritional status in underdeveloped countries. The exceptional accuracy and predictive capability of the model make it a valuable tool for early malnutrition detection, enabling data\u2010driven decision\u2010making in public health and policy formulation. By improving malnutrition diagnosis and intervention strategies, this approach has the potential to reduce morbidity and enhance child health outcomes in resource\u2010constrained settings.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/brb3.70548","type":"journal-article","created":{"date-parts":[[2025,5,13]],"date-time":"2025-05-13T03:34:54Z","timestamp":1747107294000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Advancing Nutritional Status Classification With Hybrid Artificial Intelligence: A Novel Methodological Approach"],"prefix":"10.1002","volume":"15","author":[{"given":"Md. Moddassir","family":"Alam","sequence":"first","affiliation":[{"name":"Department of Health Information Management and Technology, College of Applied Medical Sciences University of Hafr Al Batin  Hafr Al Batin Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Asif Irshad","family":"Khan","sequence":"additional","affiliation":[{"name":"Department of Computer Science Aligarh Muslim University  Aligarh India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aasim","family":"Zafar","sequence":"additional","affiliation":[{"name":"Department of Computer Science Aligarh Muslim University  Aligarh India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammad","family":"Sohail","sequence":"additional","affiliation":[{"name":"Data Architect and AI\/ML Engineer, Hewlett Packard Enterprise  Pennsylvania USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1967-1691","authenticated-orcid":false,"given":"Mohammad Tauheed","family":"Ahmad","sequence":"additional","affiliation":[{"name":"College of Medicine King Khalid University  Abha Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rezaul","family":"Azim","sequence":"additional","affiliation":[{"name":"Faculty of Science University of Chittagong  Chittagong Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,5,13]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1186\/s43093-020-00032-x"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-022-10173-w"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.3390\/children9070926"},{"key":"e_1_2_9_5_1","unstructured":"Chen T.T.He M.Benesty et al.2015. \u201cXgboost: extreme gradient boosting.\u201d R package version 0.4\u201021 no.4:1\u20134."},{"key":"e_1_2_9_6_1","unstructured":"Child Malnutrition\u2014UNICEF Dataset.2024.Child Malnutrition\u2014UNICEF Dataset taken from accessed on October 2024.https:\/\/www.kaggle.com\/datasets\/usharengaraju\/child\u2010malnutrition\u2010unicef\u2010dataset."},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1002\/jpen.2366"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1111\/agec.12777"},{"issue":"2","key":"e_1_2_9_9_1","first-page":"180","article-title":"Prevalence and Its Related Factors of Undernutrition Among under 5\u2010Year\u2010Old Children, Fars, Iran 2017\u20132018","volume":"8","author":"Derakhshandeh\u2010Rishehri S. 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