[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122899-en":3,"doc-seo-122899-105":30,"detail-sidebar-cat-0-en-105":90},{"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},122899,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",7,"Healthcare","Nutrition Deficiency Prediction using Machine Learning Techniques - Abstract","Nutrition deficiency remains a widespread public health challenge in developing nations, where household diets often lack essential macro and micronutrients required for optimal health. Limited awareness of daily food consumption contributes to nutrition gaps, motivating research that uses data from multiple health records to support prediction. The study evaluates the importance of a balanced diet and leverages Healthy Food Diversity Index (HFDI) and Household Dietary Diversity Score (HDDS). It applies machine learning models, including Random Forest, SVM, LDA, and Logistic Regression, comparing accuracy, sensitivity, and specificity while benchmarking against anthropometric classifications from the national school feeding program.","Nutrition Deficiency Prediction using Machine  \nLearning Techniques  \nU. Hemavathi  \nAssistant professor, Department of Computer Science and Engineering,  \nVel Tech RangarajanDr.Sagunthala R&D Institute of Science and Technology,  \nChennai, India  \n[e-mail: uhemav@gmail.com](e-mail: uhemav@gmail.com)  \nT.Veeramakali  \nAssociate Professor , Department of Data Science and Business Systems,  \nSchool of Computing, SRM Institute of Science and Technology,  \nKattankulathur, Chennai, India  \ne-mail: [drveeramakali@gmail.com](drveeramakali@gmail.com)  \nS. Prabu  \nAssistant Professor , Department of Computing Technologies, School of Computing,  \nSRM Institute of Science and Technology, Kattankulathur,  \nChennai, India  \ne-mail: [drprabucse@gmail.com](drprabucse@gmail.com)  \nB. S. Deepa Priya  \nAssociate Professor, Department of Computer Science and Engineering,  \nBannari Amman Institute of Technology,  \nSathyamangalam, India.  \ne-mail: [deepapriya@bitsathy.ac.in](deepapriya@bitsathy.ac.in)  \nAbstract—Despite the fact that many developing nations have experienced economic progress, Nutrition-deficiency remains a pervasive problem in the society, with millions of impoverished people's diets lacking in essential macro and micronutrients essential for optimal human health. Lack of awareness of food consumed daily causes Nutrition deficiency among general population, data from multiple health records are used for research and prediction. It investigates the importance of a well-balanced diet for our daily life. The Healthy Food Diversity Index (HFDI) is a supplement to the popular Household Dietary Diversity Score (HDDS) . It's a tool for determining the diversity of household food. The HDDS has been established as a reliable source of information, but it has several limitations as a measure of dietary diversity that is linked to nutritional quality. In this paper, various machine learning techniques such as Random Forest classifier (RF), Support-Vector Machine (SVM), Linear Discriminant Analysis (LDA) and Logistic Regression (LR) are used to predict Nutrition-Deficiency using house hold risk factors and they compared their Accuracy, Sensitivity and Specificity. The predictions were also compared to the anthropometric classifications used by the National school feeding program to prove the efficiency of the proposed approach.  \nKeywords-Nutrition Deficiency, Machine Learning, Logistic Regression, Random Forest, Prediction, HFDI, HDDS, BMI  \nI. INTRODUCTION  \nNutrition deficiency is defined as a person's intake of energy and nutrients are insufficient to meet their demands for optimal health. People with Nutrition-Deficiency can become too short for their age, dangerously thin, and lacking in vitamins, Macro nutrients (Carbohydrates, Proteins and Fats) and minerals (micronutrient deficient), someone can have multiple types of nutrition deficiency at the same time [1,2] .  \nDespite being easily preventable, Nutrition-Deficiency kills 3.1 million children each year, making it the world's leading cause of death among children. Only 24 countries account for 80% of all occurrences of child malnutrition, making this an extremely concentrated problem. Even among adults not being aware of the food that are consumed are the main reason for  \nexceeding in multiple nutrition deficiency and other health problems [3-5] .  \nNutrition is a critical component in reaching a variety of associated health and development objectives. Globally, better nutrition will reduce child and maternal mortality, enhance educational outcomes, and boost productivity and economic growth, whereas bad nutrition will perpetuate a cycle of poor health and poverty [6-8] .  \nII. RESEARCH BACKGROUND  \nFollowing are some of the research papers which came up with various methods for Nutrition-Deficiency prediction and classification. Talukder A and Ahammed B [10] conducted a survey using machine learning algorithms for predicting malnutrition status among the children under the age of","cbCaieFBEsFPF5mS","https://ap.wps.com/l/cbCaieFBEsFPF5mS","pdf",285931,1,4,"English","en",105,"# Abstract\n# Introduction\n## Definition and impact of nutrition deficiency\n## Factors and global consequences\n# Research Background\n## Prior machine learning studies\n# Proposed Method","[{\"question\":\"What problem does the paper address and why is it important?\",\"answer\":\"The paper targets nutrition deficiency as a pervasive health issue that reduces health outcomes by causing insufficient intake of essential nutrients. It highlights strong links between poor nutrition and child mortality, education, productivity, and economic growth.\"},{\"question\":\"Which machine learning techniques are used for nutrition-deficiency prediction?\",\"answer\":\"The proposed approach uses Random Forest, Support-Vector Machine, Linear Discriminant Analysis, and Logistic Regression. Model performance is compared using metrics such as accuracy, sensitivity, and specificity.\"},{\"question\":\"How do HFDI and HDDS contribute to the prediction approach?\",\"answer\":\"HDDS is treated as a source of information about household dietary diversity, and HFDI is introduced as a supplement. The paper discusses the relationship between dietary diversity measures and nutritional quality to support prediction.\"}]","Nutrition Deficiency Prediction using Machine Learning Techniques - Abstract | PDF",1785813559,10,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":28},"nutrition-deficiency-prediction-using-machine-learning-techniques-abstract","",{"@graph":36,"@context":84},[37,53,67],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/healthcare/",3,{"item":52,"name":13,"@type":43,"position":21},"https://docshare.wps.com/document/nutrition-deficiency-prediction-using-machine-learning-techniques-abstract/122899/",{"url":52,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What problem does the paper address and why is it important?","Question",{"text":74,"@type":75},"The paper targets nutrition deficiency as a pervasive health issue that reduces health outcomes by causing insufficient intake of essential nutrients. It highlights strong links between poor nutrition and child mortality, education, productivity, and economic growth.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"Which machine learning techniques are used for nutrition-deficiency prediction?",{"text":79,"@type":75},"The proposed approach uses Random Forest, Support-Vector Machine, Linear Discriminant Analysis, and Logistic Regression. Model performance is compared using metrics such as accuracy, sensitivity, and specificity.",{"name":81,"@type":72,"acceptedAnswer":82},"How do HFDI and HDDS contribute to the prediction approach?",{"text":83,"@type":75},"HDDS is treated as a source of information about household dietary diversity, and HFDI is introduced as a supplement. 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