[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127244-en":3,"doc-seo-127244-105":31,"detail-sidebar-cat-0-en-105":92},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127244,2336475104042,"Skyler","https://ap-avatar.wpscdn.com/avatar/22000c4c32af1715be0?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786537525561427321",8,"Research & Report","Machine Learning Approaches for Accurate Energy Content Prediction in Foods Using Nutritional Data","This study addresses the translation of nutritional information into practical public health policy and individual dietary decisions. Motivated by the growing burden of diet-related conditions, the research analyzes a comprehensive nutritional dataset and extracts actionable patterns. Using the USDA National Nutrient Database, it applies data preprocessing, exploratory data analysis, and multiple machine learning models to predict energy values from macronutrient composition. Findings support improved understanding of food energy composition and inform healthier dietary guidelines.","Machine Learning Approaches for Accurate Energy Content Prediction in Foods Using Nutritional Data  \nNishan Wickramasinghe Department of Science and Engineering Solent University Southampton, United Kingdom  \n[2senan77@solent.ac.uk](2senan77@solent.ac.uk)  \nShakeel Ahmad  \nDepartment of Science and Engineering Solent University Southampton, United Kingdom  \n[shakeel.ahmad@solent.ac.uk](shakeel.ahmad@solent.ac.uk)  \nRaza Hasan  \nDepartment of Science and Engineering Solent University Southampton, United Kingdom  \n[raza.hasan@solent.ac.uk](raza.hasan@solent.ac.uk)  \nSalman Mahmood Department of Computer Science Nazeer Hussain University Karachi, Pakistan  \n[salman.mahmood@nhu.edu.pk](salman.mahmood@nhu.edu.pk)  \nAbstract—This study marks a step toward more effectively translating nutritional information to inform public health policy as well as individual dietary choices. Motivated by the increase in diet-related health issues, this research aims to analyze a comprehensive nutritional dataset to uncover valuable insights. Using the USDA National Nutrient Database, the study employs data preprocessing to clean the data, exploratory data analysis to identify hidden patterns, and various machine learning models to predict nutritional values. The results demonstrate the usefulness of these models in explaining the composition data and highlight a range of trends and relationships within the observed amounts. The discussion emphasizes that these findings could be instrumental in guiding health professionals and policymakers toward healthier dietary guidelines. The significance of this research lies in its potential to advance public health through more sophisticated nutritional recommendations.  \nKeywords—Data Analytics, Food Nutrition, Machine Learning.  \nI. INTRODUCTION  \nThe analysis of nutritional data is becoming increasingly relevant to public health concerns and individual dietary alleys. This, amidst the increasing prevalence of diet-related health conditions such as obesity, diabetes, and cardiovascular diseases, has heightened nutritional interest in an individual food commodity. Because nutritional diseases, such as obesity and diabetes, are becoming increasingly prevalent in society, there has been an increasing need for the nutritional values of various individual foods. The proposed study will seek to apply newer machine learning methods to predict energy content using the data on macronutrients for which nutritional content is already available from the USDA National Nutrient Database.  \nThis study aimed to use the USDA National Nutrient Database in analyzing different foods and their nutrients that included energy, macronutrients (fat, protein), vitamins & minerals as well fiber. The machine learning (ML) methods were chosen for this study were Linear Regression, Support Vector Regression (SVR), K-Nearest Neighbors (KNN), and Deep Neural Networks (DNN) because they handle the variable relationships of macronutrients and the energy content well. For example, DNN is one option because it can model complex nonlinear associations; SVR was tested to see how it behaved to predict over small deviations in energy content. We have chosen Linear Regression due to its simplicity in modeling linear relationships, which extends a  \nuseful baseline. SVR was used to explore its strength in highdimensional spaces and small-sample learning, whereas KNN has been employed because it can capture nonlinear relationships based on proximity. Finally, DNN is a multilayer complex architecture that might help in modeling nonlinear dependencies between macronutrients and energy.  \nThis study has several main aims in exploring the work covered issues related to nutrition information. In the first part of this study, we will perform data preprocessing to cleanse and prepare the dataset for algorithmic analysis, removing any errors present. After this, Exploratory Data Analysis (EDA) will be done to find out hidden patterns or trends which are not","cbCailTFSxsAw02E","https://ap.wps.com/l/cbCailTFSxsAw02E","pdf",631309,2,1,6,"English","en",105,"# Introduction\n# Literature Review","[{\"question\":\"Which dataset is used to support energy content prediction in foods?\",\"answer\":\"The study uses the USDA National Nutrient Database as the source of nutritional information and macronutrient-related attributes.\"},{\"question\":\"What machine learning models are evaluated for predicting energy content?\",\"answer\":\"Linear Regression, Support Vector Regression (SVR), K-Nearest Neighbors (KNN), and Deep Neural Networks (DNN) are selected to model relationships between macronutrients and energy.\"},{\"question\":\"How does the study prepare the data before modeling?\",\"answer\":\"It performs data preprocessing to clean the dataset and removes errors. It then uses exploratory data analysis to identify hidden patterns and trends.\"}]","Machine Learning Approaches for Accurate Energy Content Prediction in Foods Using Nutritional Data | PDF",1785937718,15,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"machine-learning-approaches-for-accurate-energy-content-prediction-in-foods-using-nutritional-data","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,48,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":20},"https://docshare.wps.com/document/","Document",{"item":49,"name":12,"@type":44,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/machine-learning-approaches-for-accurate-energy-content-prediction-in-foods-using-nutritional-data/127244/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-22","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Which dataset is used to support energy content prediction in foods?","Question",{"text":76,"@type":77},"The study uses the USDA National Nutrient Database as the source of nutritional information and macronutrient-related attributes.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What machine learning models are evaluated for predicting energy content?",{"text":81,"@type":77},"Linear Regression, Support Vector Regression (SVR), K-Nearest Neighbors (KNN), and Deep Neural Networks (DNN) are selected to model relationships between macronutrients and energy.",{"name":83,"@type":74,"acceptedAnswer":84},"How does the study prepare the data before modeling?",{"text":85,"@type":77},"It performs data preprocessing to clean the dataset and removes errors. 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