[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117755-en":3,"doc-seo-117755-105":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},117755,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",8,"Research & Report","A Machine Learning System for Healthcare - Diabetic Retinopathy Prediction","Diabetic Retinopathy presents sight-threatening risks and demands early, appropriate action even when symptoms are limited. Traditional screening relies on human eye fundus examinations at fixed intervals and does not reflect individual risk profiles. This project explores a machine learning approach for diabetic retinopathy classification using tabular sociodemographic, clinical, and medication information instead of fundus images. Using a public UCI diabetes dataset, multiple models were benchmarked, achieving results 15–20 percentage points above prior literature and enabling earlier intervention for hospitalized patients.","Master’s Degree Program in Data Science and Advanced Analytics  \nA Machine Learning System for Healthcare  \nDiabetic Retinopathy Prediction  \nMariana Sousa Mendes Damião Albernaz  \nProject Work  \npresented as a partial requirement for obtaining the Master’s Degree Program in Data Science and Advanced  \nAnalytics  \nNOVA Information Management School Instituto Superior de Estatística e Gestão de Informação  \nUniversidade Nova de Lisboa  \nNOVA Information Management School  \nInstituto Superior de Estatística e Gestão de Informação  \nUniversidade Nova de Lisboa  \nA MACHINE LEARNING SYSTEM FOR HEALTHCARE: DIABETIC  \nRETINOPATHY PREDICTION  \nby  \nMariana Sousa Mendes Damião Albernaz  \nProject Work presented as a partial requirement for obtaining the Master’s degree in Advanced Analytics, with a Specialization in Data Science  \nSupervisor: Nuno Miguel da Conceição António  \nNovember 2022  \nSTATEMENT OF INTEGRITY  \nI hereby declare having conducted this academic work with integrity. I confirm that I have not used plagiarism or any form of undue use of information or falsification of results along the process leading to its elaboration. I further declare that I have fully acknowledge the Rules of Conduct and Code of Honor from the NOVA Information Management School.  \nAssinado por: MARIANA SOUSA MENDES DAMIÃOALBERNAZ  \n[Num. de](Num. de) Identificação: 14195416  \nData: 2022.11.24 16:35:28+00'00'  \nLisbon, 24th November 2022  \nDEDICATION  \nFor my parents, Elsa Sousa Mendes and Telmo Damião, and for my grandmother Isabel Sousa whom I adore and who has always encouraged and supported me.  \nThank you to my boyfriend Diogo Lopes, for all his love and support.  \nIn memory of my grandmother Elisa Damião, who has always been with me and whom I miss very much.  \nACKNOWLEDGMENTS  \nWorking on this final master's project proved to be a challenging route marked by little and large phases, but most significantly, it provided me with the joy of accomplishing a goal that culminated in the completion of another key chapter of my life, the master's degree.  \nAs a consequence, I am obliged to convey my profound thanks and appreciation to everyone who has given me unconditional support, including guidance, encouragement, patience, comprehension, positivity, and mental energy. Thank you all for joining me on this journey foryour incredible support, and for your strength, which has always encouraged me to move on and finish this project.  \nNonetheless, I would like to express my deepest gratitude to my parents, who have always been there for me and have always been a source of inspiration and drive for me;  \nTo my family, who constantly keeps a close eye on me;  \nTo Marco Dutra Medeiros, Ph. D., for allowing me to work on this project.  \nTo my advisors, professor Nuno António and Sílvia Rêgo, for their encouragement, availability, and guidance;  \nTo my boyfriend, Diogo Lopes, for his incentive and comprehension;  \nTo my friends Catarina Dâmaso, Iara Chande, Rodrigo Marques, Joana Sampaio, and Ricardo Eduardo for being present in my academic journey since the first day.  \nTo Rui Ramos and André Miranda from Super Bock Group, for supporting me to work on this project.  \nPlease accept my heartfelt gratitude.  \nABSTRACT  \nDiabetic Retinopathy is a condition with sight-threatening implications which requires early appropriate actions even when the symptoms are low.  \nThe traditional diagnostic procedure is the human eye fundus screening, which has a fixed interval for every diabetic and does not consider the individual risk of developing the disease. With recent academic and industry focus on the machine learning field, novel research has been performed on how to improve diabetic retinopathy diagnosis using tabular data from each patient.  \nMachine learning models have achieved outstanding results in predicting diseases by giving a risk index for each patient based on individual clinical information.  \nThis work tries to repurpose a new machine learning model fo","cbCaihWS2sTy0XuU","https://ap.wps.com/l/cbCaihWS2sTy0XuU","pdf",2667540,1,80,"English","en",105,"# Introduction\n## Thesis Objective\n## Research Questions\n# Theoretical Background\n## Artificial Intelligence & Data Science\n## Machine Learning\n## Synthetic Data\n## Deep Learning","[{\"question\":\"Why is early diagnosis of diabetic retinopathy important in this work?\",\"answer\":\"Diabetic Retinopathy can threaten sight and requires timely actions even when symptoms are low, enabling earlier intervention and faster clinical responses.\"},{\"question\":\"What data source and input type does the project use for prediction?\",\"answer\":\"The system uses tabular data per patient, including sociodemographic, clinical, and medication information, and does not use eye fundus images.\"},{\"question\":\"How was the model evaluated and what results were achieved?\",\"answer\":\"Several machine learning models were fitted and benchmarked using a public diabetes dataset from the UCI Machine Learning Repository. The results were reported as 15–20 percentage points better than those in the literature review.\"}]","A Machine Learning System for Healthcare - Diabetic Retinopathy Prediction | PDF",1785679407,202,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"a-machine-learning-system-for-healthcare-diabetic-retinopathy-prediction","",{"@graph":36,"@context":85},[37,54,68],{"@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/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/a-machine-learning-system-for-healthcare-diabetic-retinopathy-prediction/117755/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is early diagnosis of diabetic retinopathy important in this work?","Question",{"text":75,"@type":76},"Diabetic Retinopathy can threaten sight and requires timely actions even when symptoms are low, enabling earlier intervention and faster clinical responses.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What data source and input type does the project use for prediction?",{"text":80,"@type":76},"The system uses tabular data per patient, including sociodemographic, clinical, and medication information, and does not use eye fundus images.",{"name":82,"@type":73,"acceptedAnswer":83},"How was the model evaluated and what results were achieved?",{"text":84,"@type":76},"Several machine learning models were fitted and benchmarked using a public diabetes dataset from the UCI Machine Learning Repository. 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