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Subjects were split into two datasets based on baseline diagnosis—Cognitively Normal and Mild Cognitive Impairment—and deterioration at the final visit was treated as a binary outcome (deterioration vs. no deterioration). Using data from ADNI, including demographics, genetics, CSF, imaging, and neuropsychological testing, six models were built and evaluated with nested cross-validation. Best results used CART for the CN group (AUC 0.88) and Elastic Net for the MCI group (AUC 0.76).",{"@graph":69,"@context":123},[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/a-machine-learning-approach-for-predicting-deterioration-in-alzheimers-disease/290550/",{"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/a-machine-learning-approach-for-predicting-deterioration-in-alzheimers-disease/290550.png","ImageObject",300,407,{"name":92,"@type":93},"Noah","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-01","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",16,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"How is deterioration defined and measured in this study?","Question",{"text":113,"@type":114},"Deterioration is defined by the diagnosis at the final visit and modeled as a binary outcome indicating whether deterioration occurred.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"What data sources and feature types are used for prediction?",{"text":118,"@type":114},"The work uses the Alzheimer’s Disease Neuroimaging Initiative (ADNI) data, including demographics, genetics, CSF, imaging, and neuropsychological testing variables.",{"name":120,"@type":111,"acceptedAnswer":121},"Which machine learning models perform best for each baseline group?",{"text":122,"@type":114},"For the Cognitively Normal group, CART shows strong predictive ability (AUC = 0.88). For the Mild Cognitive Impairment group, Elastic Net achieves good predictive ability (AUC = 0.76).","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},290550,1789642052,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":29,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":130,"read_time":144,"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","A Machine Learning Approach for Predicting Deterioration in Alzheimer's Disease  \nHenry Musto, Daniel Stamate, Ida Pu Data Science and Soft Computing Lab, and Department of Computing Goldsmith, University of London London, United Kingdom [Email: hthom018@gold.ac.uk](Email: hthom018@gold.ac.uk)  \nDaniel Stahl Department of Biostatistics and Health Informatics Institute of Psychiatry, Psychology, and Neuroscience Kings College London London, United Kingdom  \nAbstract - This paper explores deterioration in Alzheimer’s Disease using Machine Learning. Subjects were split into two datasets based on baseline diagnosis (Cognitively Normal, Mild Cognitive Impairment), with outcome of deterioration at final visit (a binomial essentially yes/no categorisation) using data from the Alzheimer’s Disease Neuroimaging Initiative (demographics, genetics, CSF, imaging, and neuropsychological testing etc). Six machine learning models, including gradient boosting, were built, and evaluated on these datasets using a nested crossvalidation procedure, with the best performing models being put through repeated nested cross-validation at 100 iterations. We were able to demonstrate good predictive ability using CART predicting which of those in the cognitively normal group deteriorated and received a worse diagnosis (AUC = 0.88). For the mild cognitive impairment group, we were able to achieve good predictive ability for deterioration with Elastic Net (AUC = 0.76).  \nKeywords – Alzheimer’s Disease, Dementia, Applied Machine Learning, Statistical Learning  \nI. INTRODUCTION  \nAlzheimer’s Disease which accounts for 60-80% of all dementias [1], remains a pernicious threat to older people. As of 2017, it is estimated that over 50 million people globally are living with dementia [2] . Unlike other chronic diseases such as cancer and heart disease, dementia can require intensive care for many years after diagnosis. As a result, the cost of care is significantly higher as sufferers increasingly require round-the-clock care. Furthermore, the burden of caring for someone with dementia can negatively impact the wider families wellbeing, with associated detrimental effects on mental and physical health. The cost of long-term care also puts a disproportionate financial burden onto families and the wider community of those with a diagnosis. It is estimated that the cost of caring for those with dementia currently stands at 1% of global GDP [3] . Furthermore, 60% of those with dementia live in lowand middle-income countries [4] and, as these countries transition into developed economies, with the associated lengthening life expectancy and falling birth rate, the strain on these nations to care for dementia sufferers will become financially untenable.  \nIn lieu of effective treatment for Alzheimer’s disease [5], research has turned towards the possibility of early detection of Alzheimer’s biomarkers before the onset of symptoms [6] . Work in this area has noted that accurate prediction of the risk of developing Alzheimer’s disease and subsequent early interventions aimed at delaying the onset of symptoms would ease the burden of suffering as well as financial costs of care for the patient and their family. Even if no intervention were available, a prewarning of the risk of dementia would allow patients and carers to prepare for the possibility of cognitive decline, and thus may help reduce the psychological and practical effects of diagnosis. Indeed, research has suggested that the structural brain changes that precipitate Alzheimer’s symptoms may begin several years before the onset of notable symptoms [6], and this may provide an opportunity to develop techniques for assessing dementia risk.  \nThere have been several attempts to create predictive models for Alzheimer’s Disease. Mathotaarachchiet et al. used the same repository (ADNI) as the present paper to build Support Vector Machine (SVM) and Logistic Regression models with PET neuroimages for SVM and regularized ","cbCaieL2YLzEAgNh","https://ap.wps.com/l/cbCaieL2YLzEAgNh","pdf",294358,"English","# Abstract\n# I. INTRODUCTION\n## Background and public health impact\n## Motivation: early biomarker detection\n## Related work on predictive models\n## Study objective and approach","[{\"question\":\"How is deterioration defined and measured in this study?\",\"answer\":\"Deterioration is defined by the diagnosis at the final visit and modeled as a binary outcome indicating whether deterioration occurred.\"},{\"question\":\"What data sources and feature types are used for prediction?\",\"answer\":\"The work uses the Alzheimer’s Disease Neuroimaging Initiative (ADNI) data, including demographics, genetics, CSF, imaging, and neuropsychological testing variables.\"},{\"question\":\"Which machine learning models perform best for each baseline group?\",\"answer\":\"For the Cognitively Normal group, CART shows strong predictive ability (AUC = 0.88). For the Mild Cognitive Impairment group, Elastic Net achieves good predictive ability (AUC = 0.76).\"}]","A Machine Learning Approach for Predicting Deterioration in Alzheimer's Disease | PDF",15]