Predicting Alzheimers Disease Diagnosis Risk over Time with Survival Machine Learning on the ADNI Cohort

Henry Musto, Daniel Stamate, Ida Pu, Daniel Stahl
6/17/2023
cs.LG

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Abstract

The rise of Alzheimers Disease worldwide has prompted a search for efficient tools which can be used to predict deterioration in cognitive decline leading to dementia. In this paper, we explore the potential of survival machine learning as such a tool for building models capable of predicting not only deterioration but also the likely time to deterioration. We demonstrate good predictive ability (0.86 C-Index), lending support to its use in clinical investigation and prediction of Alzheimers Disease risk.

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Cite this paper

@article{musto2023predicting,
  title  = {Predicting Alzheimers Disease Diagnosis Risk over Time with Survival Machine Learning on the ADNI Cohort},
  author = {Henry Musto and Daniel Stamate and Ida Pu and Daniel Stahl},
  year   = {2023},
  eprint = {2306.10326},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2306.10326v1}
}

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