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It addresses limitations of population-level guidelines that ignore lifestyle, age, gender, and activity differences, which contributes to preventable chronic diseases. Using the American Gut Project dataset and six models, the work identifies the Random Forest classifier as most accurate (73%) after hyperparameter optimization and SMOTE. K-Means clustering reveals two diet patterns, “Healthy” and “Unhealthy,” with separable clusters supported by PCA and lifestyle features such as exercise frequency and age.",{"@graph":69,"@context":127},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/personalized-health-and-nutrition-recommendations-using-machine-learning-msc-research-project-submission-sheet/290539/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/personalized-health-and-nutrition-recommendations-using-machine-learning-msc-research-project-submission-sheet/290539.png","ImageObject",300,407,{"name":92,"@type":93},"Noah","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-09","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",11,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119,123],{"name":110,"@type":111,"acceptedAnswer":112},"What problem does the research address?","Question",{"text":113,"@type":114},"It addresses the gap between generic public dietary guidelines and the need for individualized nutrition advice that reflects differences in age, sex, lifestyle, and support context, since this gap contributes to preventable chronic diseases.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"Which dataset and models were used?",{"text":118,"@type":114},"The study uses the American Gut Project dataset and evaluates six machine learning models: Random Forest, Gradient Boosting, SVM, Neural Networks, CatBoost, and LightGBM.",{"name":120,"@type":111,"acceptedAnswer":121},"What were the key findings about diet patterns?",{"text":122,"@type":114},"Random Forest achieved 73% overall accuracy after hyperparameter optimization and SMOTE, and clustering with K-Means revealed two dietary patterns: “Healthy” (high protein and fiber) and “Unhealthy” (high fat and carbohydrate).",{"name":124,"@type":111,"acceptedAnswer":125},"What does the research plan to do next?",{"text":126,"@type":114},"Future work focuses on improving interpretable model solutions and using them to build tools that encourage people to adopt better diets.","https://schema.org",{"og:url":83,"og:type":129,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":131,"canonical":83},"index,follow",{"doc_id":133,"site_id":62},290539,1789641967,{"code":4,"msg":5,"data":136},{"doc_id":133,"user_id":137,"nickname":92,"user_avatar":138,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":139,"file_id":140,"file_url":141,"file_type":142,"file_size":143,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":144,"language":145,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":146,"faqs":147,"seo_title":148,"seo_description":67,"update_tm":134,"read_time":149,"preview_page_count":4},8796095462418,"https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780","Personalized Health and Nutrition  \nRecommendations Using Machine Learning  \nFinal MSc Research ProjectData Analytics  \nSanjay Rajendra RautStudent ID: 22196901  \nSchool of ComputingNational College of Ireland  \nSupervisor: Dr. Abid Yaqoob  \nNational College of IrelandMSc Project Submission SheetSchool of Computing  \n| Student Name:   | Sanjay Rajendra Raut   |\n| --- | --- |\n| Student ID:   | 22196901   |\n| Programme:   | Masters in data Analytics   |\n| Year:   | 2024   |\n| Module:   | Msc In Research Project   |\n| Supervisor:   | Dr. Abid Yaqoob   |\n| Submission Due Date:   | 29/01/2025   |\n| Project Title:   | Personalized Health and Nutrition RecommendationsUsing Machine Learning   |\n| Word Count:   |  |\n| Page Count:   | 24   |\n\nI hereby certify that the information contained in this (my submission) isinformation pertaining to research I conducted for this project. All information other thanmy own contribution will be fully referenced and listed in the relevant bibliography sectionat the rear of the project.  \nALL internet material must be referenced in the bibliography section. Students arerequired to use the Referencing Standard specified in the report template. To use otherauthor's written or electronic work is illegal (plagiarism) and may result in disciplinaryaction.  \nSanjay Raut  \nSignature:    \n29 /01/2025  \nDate:    \nPLEASE READ THE FOLLOWING INSTRUCTIONS AND CHECKLIST  \n\n| Attach a completed copy of this sheet to each project (including multiplecopies)   | □   |\n| --- | --- |\n| Attach a Moodle submission receipt of the online projectsubmission, to each project (including multiple copies) .   | □   |\n| You must ensure that you retain a HARD COPY of the project,both for your own reference and in case a project is lost or mislaid. It isnot sufficient to keep a copy on computer.   | □   |\n\nAssignments that are submitted to the Programme Coordinator Office must be placedinto the assignment box located outside the office.  \n\n| Office Use Only   |  |\n| --- | --- |\n| Signature:   |  |\n| Date:   |  |\n| Penalty Applied (if applicable):   |  |\n\n# Personalized Health and Nutrition\n\nRecommendations Using Machine Learning  \nSanjay Rajendra Raut  \n22196901  \nAbstract  \nIt is our vision that dependence on individualized nutrition is essential in order to positivelyimpact overall health and reduce chronically debilitating diseases. The present dietaryguideline for the population dies not take into account the lifestyle, age, gender, and activitylevel of the population. In this work, we present an approach to adopting a machine learningmodel for the prediction of the consumers’ dietary behaviours and recommending suitablediets. To achieve this aim, we employed the American Gut Project dataset to apply six MachineLearning models namely; Random Forest, Gradient Boosting, SVM, Neural Networks,CatBoost, and LightGBM. The Random Forest model, after hyperparameter optimization andSMOTE method, was found to be the most accurate classifier with overall accuracy of 73%,precision, recall and F1 score values all similar to each other. Our clustering analysis,performed with K-Means, revealed two distinct dietary patterns. More specifically the pre -determined criteria were ‘Healthy’(High protein and fiber content) and‘Unhealthy’ (High fatand carbohydrate content) . Principal Component Analysis (PCA) was then used to representthese clusters and as seen, the clusters are separable. Some of the measures used in order toclassify the diets included the frequency of exercise, the type of diet and age. This researchcloses the gap between the application of machine learning and personalized nutrition, with acomplete program to analyse the intricacies of nutrition. Further work will focus on improvinginterpretable model solutions and utilizing them for the creation of tools to encourage peopleto adopt better diets.  \n## 1 Introduction\n\nDietary habits are among the most important factors determining health, yet we cannotadequately provide customized di","cbCaicbVehJxVLGt","https://ap.wps.com/l/cbCaicbVehJxVLGt","pdf",1002482,26,"English","# 1 Introduction\n## Abstract\n## 1 Introduction","[{\"question\":\"What problem does the research address?\",\"answer\":\"It addresses the gap between generic public dietary guidelines and the need for individualized nutrition advice that reflects differences in age, sex, lifestyle, and support context, since this gap contributes to preventable chronic diseases.\"},{\"question\":\"Which dataset and models were used?\",\"answer\":\"The study uses the American Gut Project dataset and evaluates six machine learning models: Random Forest, Gradient Boosting, SVM, Neural Networks, CatBoost, and LightGBM.\"},{\"question\":\"What were the key findings about diet patterns?\",\"answer\":\"Random Forest achieved 73% overall accuracy after hyperparameter optimization and SMOTE, and clustering with K-Means revealed two dietary patterns: “Healthy” (high protein and fiber) and “Unhealthy” (high fat and carbohydrate).\"},{\"question\":\"What does the research plan to do next?\",\"answer\":\"Future work focuses on improving interpretable model solutions and using them to build tools that encourage people to adopt better diets.\"}]","Personalized Health and Nutrition Recommendations Using Machine Learning - MSc Research Project Submission Sheet | PDF",66]