Early detection and characterization of Alzheimer's disease in clinical scenarios using Bioprofile concepts and K-means

Javier Escudero, John P Zajicek, Emmanuel Ifeachor

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

23 Citations (Scopus)

Abstract

Alzheimer's Disease (AD) is the most common neurodegenerative disease in elderly people. There is a need for objective means to detect AD early to allow targeted interventions and to monitor response to treatment. To help clinicians in these tasks, we propose the creation of the Bioprofile of AD. A Bioprofile should reveal key patterns of a disease in the subject's biodata. We applied k-means clustering to data features taken from the ADNI database to divide the subjects into pathologic and non-pathologic groups in five clinical scenarios. The preliminary results confirm previous findings and show that there is an important AD pattern in the biodata of controls, AD, and Mild Cognitive Impairment (MCI) patients. Furthermore, the Bioprofile could help in the early detection of AD at the MCI stage since it divided the MCI subjects into groups with different rates of conversion to AD.

Original languageEnglish
Title of host publicationProceedings of the 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Pages6470-3
Number of pages4
Volume2011
ISBN (Electronic)978-1-4244-4122-8
DOIs
Publication statusPublished - 2011

Keywords

  • Adult
  • Algorithms
  • Alleles
  • Alzheimer Disease
  • Biomarkers
  • Cluster Analysis
  • Cognition Disorders
  • Female
  • Humans
  • Magnetic Resonance Imaging
  • Male
  • Middle Aged
  • Mild Cognitive Impairment
  • Neuropsychological Tests
  • Positron-Emission Tomography
  • Reproducibility of Results
  • Tomography, X-Ray Computed

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