[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128232-en":3,"doc-seo-128232-105":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},128232,2336475104736,"Quinn","https://ap-avatar.wpscdn.com/avatar/22000c4c5e0e5b17e70?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786591360781797222",7,"Healthcare","Using Machine Learning for Predicting Alzheimer's Disease Among Older Adults - Master’s thesis","This master’s thesis investigates the potential of machine learning to predict Alzheimer’s disease among older adults before symptoms appear. Five established classification models are developed and evaluated to find the most effective algorithms, followed by validation through comparison with existing research. Key predictors are identified, with analysis indicating strong relationships between cognitive function measures and increased risk. Results show that extreme gradient boosting achieves the best overall performance, while random forest delivers the highest AUC-ROC score.","Master’s thesis  \nNT NU  \nNorwegian University of Science and Technology Faculty of Engineering  \nDepartment of Mechan ica l and Industrial Engineering  \nSynne Sjaavik and Jennifer Vo  \nUsing Machine Learning for Predicting Alzheimer's Disease Among Older Adults  \nMaster’s thesis in Engineering & ICT Supervisor: Lars Tingelstad  \nCo-supervisor: Ulrik Wisløff and Atefe Tari June 2024  \nSynne Sjaavik and Jennifer Vo  \nUsing Machine Learning for Predicting Alzheimer's Disease Among Older Adults  \nMaster’s thesis in Engineering & ICT Supervisor: Lars Tingelstad  \nCo-supervisor: Ulrik Wisløff and Atefe Tari June 2024  \nNorwegian University of Science and Technology Faculty of Engineering  \nDepartment of Mechanical and Industrial Engineering  \nPreface  \nThis master’s thesis is part of the course TPK4960 Robotics and Automation, Master’s Thesis offered by the Department of Mechanical and Industrial Engineering at the Norwegian University of Science and Technology (NTNU) . In addition to our supervisor from the Department of Mechanical and Industrial Engineering, Faculty of Engineering Lars Tingelstad, we also had two co-supervisors Ulrik Wisløff and Atefe Tari from the Department of Circulation and Medical Imaging, Faculty of Medicine and Health Science during this project. In cooperation with Tingelstad, Wisløff, and Tari, this research question was formulated to capture our interest in health and how machine learning can be useful in different health aspects.  \nWe would like to thank our supervisor Lars Tingelstad for giving us the freedom to choose a project within our interest. He has shown trust in our work and supported our choices during the project. We have had several informative and instructive discussions leading to constructive feedback and insightful advice. His guidance has been invaluable for our results. Additionally, we would like to thank our co-supervisors Ulrik Wisløff and Atefe Tari for their guidance related to the medical aspect of this project and for providing us with the data set. Our meetings have been valuable for gaining necessary insight on the topic and discussing the results from a medical aspect.  \nWe would also like to acknowledge the use of AI tools for this project. We utilized ChatGPT and Grammarly to ensure correct grammar and to enhance the text flow. Additionally, ChatGPThas been used to improve code quality and debugging.  \nSammendrag  \nHovedfokuset i denne studien var å utforske mulighetene til å bruke maskinlæring for å predikere Alzheimers sykdom blant eldre, før symptomene oppstår. Vi startet med å utvikle og evaluerefem maskinlæringsmodeller som er mye brukt og har gitt gode resultater i tidligere studier. Dette gjorde vi for å finne de mest effektive algoritmene. Deretter validerte vi metoden vår ved å sammenligne implementerte modeller med eksisterende modeller. Dette gjorde av vi klarte å identifisere de viktigste faktorene for å predikere Alzheimers sykdom.  \nØkningen i antall eldre i Norge har ført til en økning i tilfeller av demens. Alzheimers sykdom er den viktigste årsaken til demens blant personer over 65 år. I 2020 ble omtrent 100 000 personer diagnostisert med demens i Norge. Dette tallet forventes å øke de neste tiårene på grunn av økningen i antall eldre. Omsorg og pleie relatert til en person med demens koster samfunnetomtrent 362 800 norske kroner per år. Sykdommen blir ofte påvist etter at symptomene har oppstått. Siden det ikke finnes en kur for demens eller Alzheimers sykdom, er det viktig å oppdage tidlige tegn på sykdommene for å kunne starte behandling og bremse utviklingen. Dagens utredning av demens og Alzheimers sykdom er både kostbare og tidkrevende. Behovet for bruk av ny teknologi innen tidlig diagnostisering er derfor stort.  \nI prosjektet brukte vi data fra Generasjon 100-studien, hentet fra forskningsgruppen Cardiac Excercise Research Group, som fulgte 1 567 deltakere i aldersgruppen 70 til 77 år. Deltakernebodde i Trondheim og ble observert over fem år. 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