[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117034-en":3,"doc-seo-117034-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117034,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",7,"Healthcare","Machine learning model for predicting fetal nutritional status","Malnutrition remains a major driver of child mortality in Tanzania and other developing countries, particularly during the first five years of life. This research develops a machine learning model to predict fetal nutritional status by classifying children in a test dataset as “malnourished” or “nourished.” Multiple algorithms, including AdaBoost, Logistic Regression, Support Vector Machine, Random Forest, Naïve Bayes, Decision Tree, K-nearest neighbor, and Stochastic Gradient Descent, are evaluated using accuracy, sensitivity, and specificity. Results indicate Random Forest achieves about 98% and supports targeted nutritional attention.","Article  \nMachine learning model for predicting fetal nutritional status  \nB. Selemani, D. Machuve, N. Mduma  \nNelson Mandela African Institution of Science and Technology, Arusha Tanzania  \nE-mail: [selemanib@nm-aist.ac.tz](selemanib@nm-aist.ac.tz); [dina.machuve@nm-aist.ac.tz](dina.machuve@nm-aist.ac.tz), [neema.mduma@nm-aist.ac.tz](neema.mduma@nm-aist.ac.tz)  \nReceived 25 April 2023; Accepted 20 July 2023; Published online 23 October 2023; Published 1 March 2024  \nAbstract  \nMalnutrition tends to be one of the most important reasons for child mortality in Tanzania and other developing countries, in most cases during the first five years of life. This research was conducted todevelop machine learning model for predicting fetal nutritional status. Several machine learning techniques such as AdaBoost, Logistic Regression, Support Vector Machine, Random Forest, Naïve Bayes, Decision Tree, K-nearest neighbor and Stochastic Gradient Descent, were used to categorize the children in the test dataset as “malnourished” or“nourished”. The accuracy, sensitivity, and specificity of these algorithms’ prediction abilities were comparedusing performance measures such as accuracy, sensitivity, and specificity. Results show that malnutrition status can be predicted using Random Forest machine learning technique which was about 98% and brings positive impact to the society. The study findings indicated a need for more attention on nutrition to expected mothers and children under five to be well administered with the government and the society at large by putting relevance to the suggestion that cooperation between government organizations, academia, and industry is necessary to provide sufficient infrastructure support for the future society.  \nKeywords malnutrition; mobile application; machine learning; Tanzania.  \nComputational Ecology and Software  \nISSN 2220 721X  \nURL: [http://www.iaees.org/publications/journals/ces/online version.asp](http://www.iaees.org/publications/journals/ces/online version.asp)  \nRSS: [http://www.iaees.org/publications/journals/ces/rss.xml](http://www.iaees.org/publications/journals/ces/rss.xml)  \nE mail: [ces@iaees.org](ces@iaees.org)  \nEditor in Chief: WenJun Zhang  \nPublisher: International Academy of Ecology and Environmental Sciences  \n1 Introduction  \nMalnutrition remains endemic in many poor nations, particularly in Sub-Saharan Africa and parts of Asia. According to UNICEF, WHO and the World Bank, Stunting affected an estimated 22.0% or 149.2 million children under five (5yrs) globally in 2020, over a third resided in Africa (World Health Organization, 2021) . In Tanzania, around 3 million children under five years of age are estimated to be stunted due to inadequate nutrition. The World Health Report (2003, 2003) argued that in Mainland, the level of stunting was considered tobe very high (≥30%) in 15 regions out of 26 (Tanzania Food and Nutrition Center, 2015) . Tanzania National Nutrition Survey (2018, 2019) reported that stunting affect about 10.0 % of children countrywide. Whereas the most affected regions with a prevalence of stunting exceeding 40% were mentioned to be Ruvuma (41.0%),  \nIAEES [www.iaees.org](www.iaees.org)  \nIringa (47.1%), Rukwa (47.9%), Kigoma (42.3%), Njombe (53.6%) and Songwe (43.3%) . While in Zanzibar, stunting rates were ranging from 20.4% in Stone Town to 23.8% in Unguja North.  \nProper diagnosis and treatment of malnutrition could reduce the risk of malnutrition. Various medical devices have been developed to assess children's nutritional status. The primary goal of the diagnostic procedures is to correctly predict the associated disease. Machine learning (ML), a scientific approach that combines artificial intelligence and statistical learning research, is a method for investigating large amounts of data in order to discover previously unknown relationships or patterns (Zhang, 2010; Zou et al., 2018; Alghamdiet al., 2017) .  \nIn medical research, various machine learning alg","cbCairDpPvpFDhpT","https://ap.wps.com/l/cbCairDpPvpFDhpT","pdf",1311201,1,9,"English","en",105,"# Abstract\n# 1 Introduction\n## Malnutrition prevalence and background\n## Role of machine learning in medical prediction\n# 2 Materials and Methods\n## Study setting and data collection\n## Data cleaning and analysis","[{\"question\":\"What problem does the study address?\",\"answer\":\"The study targets malnutrition as a major cause of child mortality, aiming to predict fetal nutritional status for early identification.\"},{\"question\":\"Which machine learning methods are compared in the research?\",\"answer\":\"AdaBoost, Logistic Regression, Support Vector Machine, Random Forest, Naïve Bayes, Decision Tree, K-nearest neighbor, and Stochastic Gradient Descent are used and compared.\"},{\"question\":\"Which algorithm provides the best prediction performance?\",\"answer\":\"Random Forest is reported to predict malnutrition status with an accuracy of about 98%.\"}]","Machine learning model for predicting fetal nutritional status | 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problem does the study address?","Question",{"text":75,"@type":76},"The study targets malnutrition as a major cause of child mortality, aiming to predict fetal nutritional status for early identification.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning methods are compared in the research?",{"text":80,"@type":76},"AdaBoost, Logistic Regression, Support Vector Machine, Random Forest, Naïve Bayes, Decision Tree, K-nearest neighbor, and Stochastic Gradient Descent are used and compared.",{"name":82,"@type":73,"acceptedAnswer":83},"Which algorithm provides the best prediction performance?",{"text":84,"@type":76},"Random Forest is reported to predict malnutrition status with an accuracy of about 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