{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/105263"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/105263","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Predicting progressions and clinical subtypes of Alzheimer’s disease using machine learning","abstract":"Alzheimer’s disease is a degenerative brain disease which impairs a person’s ability to perform day to day activities. Research has shown AD to be a heterogeneous condition, having a high variation in terms of the symptoms and disease progression rate. Treating Alzheimer's disease (AD) is especially challenging due to these variations present in the disease progression stages. The clinical symptoms of AD show marked variability in terms of patients’ age, disease span, progression velocity and types of memory, cognitive and depression related features. Hence, the idea of personalized clinical care, with individualized risk, progression and prediction related patient advice in AD is narrow. This facilitates the yet unfulfilled need for an early prediction of the disease course to assist its treatment and tailor therapy options to the progression rate. Additionally, there are ramifications in clinical trial design when considering the high heterogeneity of disease manifestation and progression. Recent developments in machine learning techniques provide a huge potential, not only to predict the onset and progression of Alzheimer's disease but also to classify the disease into different etiological subtypes. The advancement of these prediction models have the potential to impact clinical decision making and improve healthcare resource allocation. It will also lead to the development of personalized clinical care and counseling for patients, hopefully reducing AD treatment costs. The suggested work clusters patients in distinct and multifaceted progression subgroups of Alzheimer's disease and discusses an approach to predict the progression stage from baseline diagnosis through the implementation of machine learning techniques. By applying machine learning algorithms on the extensive clinical observations available in the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, we parse the progression space for the Alzheimer’s disease into low, moderate and high disease progressors. This work suggests that the myriad of clinically reported symptoms we summarize in the Alzheimer's Disease progression space correspond directly to memory and cognition measurements classically used to monitor disease onset and progression. The proposed work concludes notably accurate prediction of disease progression after four years from the first 12 months of post-diagnosis data (area under receiver operating characteristic (ROC) curve of 0.90±0.02 for Controls, 0.96±0.04 for High rate, 0.90±0.04 for Moderate rate 0.83±0.06 for Low rate). We validate our model through five-fold cross-validation to obtain a robust prediction of membership into these progression subtypes. These machine learning techniques will assist the medical practitioners to classify different progression rates within patients and allow for more efficient and unique care delivery. With additional information about the onset rate of AD at hand, doctors may alter their treatments to better suit the patients. The predictive tests discussed in this report not only allow for early detection but also facilitate the characterization of distinct disease subtypes relating to trajectories of disease progression. This will lead to improved clinical trial design and reducing skyrocketing healthcare costs in the future.","abstract_html":"Alzheimer’s disease is a degenerative brain disease which impairs a person’s ability to perform day to day activities. Research has shown AD to be a heterogeneous condition, having a high variation in terms of the symptoms and disease progression rate. Treating Alzheimer's disease (AD) is especially challenging due to these variations present in the disease progression stages. The clinical symptoms of AD show marked variability in terms of patients’ age, disease span, progression velocity and types of memory, cognitive and depression related features. Hence, the idea of personalized clinical care, with individualized risk, progression and prediction related patient advice in AD is narrow. This facilitates the yet unfulfilled need for an early prediction of the disease course to assist its treatment and tailor therapy options to the progression rate. Additionally, there are ramifications in clinical trial design when considering the high heterogeneity of disease manifestation and progression. Recent developments in machine learning techniques provide a huge potential, not only to predict the onset and progression of Alzheimer's disease but also to classify the disease into different etiological subtypes. The advancement of these prediction models have the potential to impact clinical decision making and improve healthcare resource allocation. It will also lead to the development of personalized clinical care and counseling for patients, hopefully reducing AD treatment costs. The suggested work clusters patients in distinct and multifaceted progression subgroups of Alzheimer's disease and discusses an approach to predict the progression stage from baseline diagnosis through the implementation of machine learning techniques. By applying machine learning algorithms on the extensive clinical observations available in the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, we parse the progression space for the Alzheimer’s disease into low, moderate and high disease progressors. This work suggests that the myriad of clinically reported symptoms we summarize in the Alzheimer's Disease progression space correspond directly to memory and cognition measurements classically used to monitor disease onset and progression. The proposed work concludes notably accurate prediction of disease progression after four years from the first 12 months of post-diagnosis data (area under receiver operating characteristic (ROC) curve of 0.90±0.02 for Controls, 0.96±0.04 for High rate, 0.90±0.04 for Moderate rate 0.83±0.06 for Low rate). We validate our model through five-fold cross-validation to obtain a robust prediction of membership into these progression subtypes. These machine learning techniques will assist the medical practitioners to classify different progression rates within patients and allow for more efficient and unique care delivery. With additional information about the onset rate of AD at hand, doctors may alter their treatments to better suit the patients. The predictive tests discussed in this report not only allow for early detection but also facilitate the characterization of distinct disease subtypes relating to trajectories of disease progression. This will lead to improved clinical trial design and reducing skyrocketing healthcare costs in the future.","abstract_has_math":false,"creators":["Satone, Vipul Kishor"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Industrial Engineering","degree_department":null,"school":null,"contributors":["Campbell, Roy H."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019-08-23T20:48:26Z","date_published":"2019-08-23T20:48:26Z","updated_at":"2026-07-22T22:24:44Z","subjects":["Alzheimer's disease, machine learning, disease progression, disease prediction"],"languages":["en"],"rights":["Copyright 2019 Vipul Kishor Satone"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/105263","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Campbell, Roy H."]},{"key":"dc:creator","label":"Author","values":["Satone, Vipul Kishor"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2019-08-23T20:48:26Z","2021-08-24T09:15:38Z","2019-04-25","2019-05"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Industrial Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Alzheimer's disease, machine learning, disease progression, disease prediction"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2019 Vipul Kishor Satone"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/105263"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Alzheimer’s disease is a degenerative brain disease which impairs a person’s ability to perform day to day activities. Research has shown AD to be a heterogeneous condition, having a high variation in terms of the symptoms and disease progression rate. Treating Alzheimer's disease (AD) is especially challenging due to these variations present in the disease progression stages. The clinical symptoms of AD show marked variability in terms of patients’ age, disease span, progression velocity and types of memory, cognitive and depression related features. Hence, the idea of personalized clinical care, with individualized risk, progression and prediction related patient advice in AD is narrow. This facilitates the yet unfulfilled need for an early prediction of the disease course to assist its treatment and tailor therapy options to the progression rate. Additionally, there are ramifications in clinical trial design when considering the high heterogeneity of disease manifestation and progression. Recent developments in machine learning techniques provide a huge potential, not only to predict the onset and progression of Alzheimer's disease but also to classify the disease into different etiological subtypes. The advancement of these prediction models have the potential to impact clinical decision making and improve healthcare resource allocation. It will also lead to the development of personalized clinical care and counseling for patients, hopefully reducing AD treatment costs. The suggested work clusters patients in distinct and multifaceted progression subgroups of Alzheimer's disease and discusses an approach to predict the progression stage from baseline diagnosis through the implementation of machine learning techniques. By applying machine learning algorithms on the extensive clinical observations available in the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, we parse the progression space for the Alzheimer’s disease into low, moderate and high disease progressors. This work suggests that the myriad of clinically reported symptoms we summarize in the Alzheimer's Disease progression space correspond directly to memory and cognition measurements classically used to monitor disease onset and progression. The proposed work concludes notably accurate prediction of disease progression after four years from the first 12 months of post-diagnosis data (area under receiver operating characteristic (ROC) curve of 0.90±0.02 for Controls, 0.96±0.04 for High rate, 0.90±0.04 for Moderate rate 0.83±0.06 for Low rate). We validate our model through five-fold cross-validation to obtain a robust prediction of membership into these progression subtypes. These machine learning techniques will assist the medical practitioners to classify different progression rates within patients and allow for more efficient and unique care delivery. With additional information about the onset rate of AD at hand, doctors may alter their treatments to better suit the patients. The predictive tests discussed in this report not only allow for early detection but also facilitate the characterization of distinct disease subtypes relating to trajectories of disease progression. This will lead to improved clinical trial design and reducing skyrocketing healthcare costs in the future.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2021-05-01","The student, Vipul Satone, accepted the attached license on 2019-04-24 at 19:02.","The student, Vipul Satone, submitted this Thesis for approval on 2019-04-24 at 19:58.","This Thesis was approved for publication on 2019-04-25 at 14:35.","DSpace SAF Submission Ingestion Package generated from Vireo submission #13896 on 2019-08-22 at 16:23:53","Made available in DSpace on 2019-08-23T20:48:26Z (GMT). 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Research has shown AD to be a heterogeneous condition, having a high variation in terms of the symptoms and disease progression rate. Treating Alzheimer's disease (AD) is especially challenging due to these variations present in the disease progression stages. The clinical symptoms of AD show marked variability in terms of patients’ age, disease span, progression velocity and types of memory, cognitive and depression related features. Hence, the idea of personalized clinical care, with individualized risk, progression and prediction related patient advice in AD is narrow. This facilitates the yet unfulfilled need for an early prediction of the disease course to assist its treatment and tailor therapy options to the progression rate. Additionally, there are ramifications in clinical trial design when considering the high heterogeneity of disease manifestation and progression. Recent developments in machine learning techniques provide a huge potential, not only to predict the onset and progression of Alzheimer's disease but also to classify the disease into different etiological subtypes. The advancement of these prediction models have the potential to impact clinical decision making and improve healthcare resource allocation. It will also lead to the development of personalized clinical care and counseling for patients, hopefully reducing AD treatment costs. The suggested work clusters patients in distinct and multifaceted progression subgroups of Alzheimer's disease and discusses an approach to predict the progression stage from baseline diagnosis through the implementation of machine learning techniques. By applying machine learning algorithms on the extensive clinical observations available in the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, we parse the progression space for the Alzheimer’s disease into low, moderate and high disease progressors. This work suggests that the myriad of clinically reported symptoms we summarize in the Alzheimer's Disease progression space correspond directly to memory and cognition measurements classically used to monitor disease onset and progression. The proposed work concludes notably accurate prediction of disease progression after four years from the first 12 months of post-diagnosis data (area under receiver operating characteristic (ROC) curve of 0.90±0.02 for Controls, 0.96±0.04 for High rate, 0.90±0.04 for Moderate rate 0.83±0.06 for Low rate). We validate our model through five-fold cross-validation to obtain a robust prediction of membership into these progression subtypes. These machine learning techniques will assist the medical practitioners to classify different progression rates within patients and allow for more efficient and unique care delivery. With additional information about the onset rate of AD at hand, doctors may alter their treatments to better suit the patients. The predictive tests discussed in this report not only allow for early detection but also facilitate the characterization of distinct disease subtypes relating to trajectories of disease progression. This will lead to improved clinical trial design and reducing skyrocketing healthcare costs in the future.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2021-05-01","The student, Vipul Satone, accepted the attached license on 2019-04-24 at 19:02.","The student, Vipul Satone, submitted this Thesis for approval on 2019-04-24 at 19:58.","This Thesis was approved for publication on 2019-04-25 at 14:35.","DSpace SAF Submission Ingestion Package generated from Vireo submission #13896 on 2019-08-22 at 16:23:53","Made available in DSpace on 2019-08-23T20:48:26Z (GMT). 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