Musto, Henry; Stamate, Daniel; Pu, Ida and Stahl, Daniel. 2022. 'A Machine Learning Approach for Predicting Deterioration in Alzheimer's Disease'. In: 20th IEEE International Conference on Machine Learning and Applications (ICMLA). Pasadena, CA, United States 13-16 December 2021. [Conference or Workshop Item]

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Abstract or Description

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 cross-validation 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).

Item Type:	Conference or Workshop Item (Paper)
Identification Number (DOI):	https://doi.org/10.1109/ICMLA52953.2021.00232
Additional Information:	© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Keywords:	Alzheimer’s Disease, Dementia, Applied Machine Learning, Statistical Learning
Departments, Centres and Research Units:	Computing
Dates:	Date Event 23 September 2021 Accepted 25 January 2022 Published
Event Location:	Pasadena, CA, United States
Date range:	13-16 December 2021
Item ID:	31525
Date Deposited:	25 Feb 2022 15:34
Last Modified:	28 Oct 2024 13:35
URI:	https://research.gold.ac.uk/id/eprint/31525

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