A Machine Learning Approach to Understanding the Progression of Alzheimer’s Disease

Vineeta Peddinti, Robin Qiu

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Alzheimer’s is a type of dementia that progressively destroys memory cells and other important mental functions. It is a degenerative process involving different stages and it is critical to predict the progression for developing lifestyle change guidance or treatments to slow it down given that there is no cure. Although there has been lot of research going on for developing prediction models using deep learning and machine learning techniques focusing on the severity staging prediction of Alzheimer’s Disease (AD), this paper investigates mainly on the time factor for progressing to the next stage. A machine learning model is applied to analyzing the factors contributing to the progression using the clinical and neuropsychological data provided by the National Alzheimer’s Coordinating Center. In this study, given the metrics to assess the AD stage and the clinical diagnoses of the patient’s historical visits, the number of months it takes for a patient to progress to the next stage is predicted. The most important factors that contribute to the progression of the disease to the next stage are uncovered, aimed at helping AD patients weaken their disease progression.

Original languageEnglish (US)
Title of host publicationAI and Analytics for Public Health - Proceedings of the 2020 INFORMS International Conference on Service Science
EditorsHui Yang, Robin Qiu, Weiwei Chen
PublisherSpringer Science and Business Media B.V.
Number of pages12
ISBN (Print)9783030751654
StatePublished - 2022
EventINFORMS International Conference on Service Science, ICSS 2020 - Virtual, Online
Duration: Dec 19 2020Dec 21 2020

Publication series

NameSpringer Proceedings in Business and Economics
ISSN (Print)2198-7246
ISSN (Electronic)2198-7254


ConferenceINFORMS International Conference on Service Science, ICSS 2020
CityVirtual, Online

All Science Journal Classification (ASJC) codes

  • General Business, Management and Accounting
  • Economics, Econometrics and Finance(all)


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