Predicting probable Alzheimer's disease using linguistic deficits and biomarkers.

Journal: BMC bioinformatics
Published Date: Jan 14, 2017

Abstract

BACKGROUND: The manual diagnosis of neurodegenerative disorders such as Alzheimer's disease (AD) and related Dementias has been a challenge. Currently, these disorders are diagnosed using specific clinical diagnostic criteria and neuropsychological examinations. The use of several Machine Learning algorithms to build automated diagnostic models using low-level linguistic features resulting from verbal utterances could aid diagnosis of patients with probable AD from a large population. For this purpose, we developed different Machine Learning models on the DementiaBank language transcript clinical dataset, consisting of 99 patients with probable AD and 99 healthy controls.

Authors

  • Sylvester O Orimaye
    Intelligent Health Research Group, School of Information Technology, Monash University, Jalan Lagoon Selatan, Bandar Sunway, 47500, Malaysia. sylvester.orimaye@monash.edu.
  • Jojo S-M Wong
    Intelligent Health Research Group, School of Information Technology, Monash University, Jalan Lagoon Selatan, Bandar Sunway, 47500, Malaysia.
  • Karen J Golden
    Jeffrey Cheah School of Medicine and Health Sciences, Monash University, Jalan Lagoon Selatan, Bandar Sunway, 47500, Malaysia.
  • Chee P Wong
    Jeffrey Cheah School of Medicine and Health Sciences, Monash University, Jalan Lagoon Selatan, Bandar Sunway, 47500, Malaysia.
  • Ireneous N Soyiri
    Centre for Medical Informatics, Usher Institute for Population Health Sciences & Informatics, The University of Edinburgh, Teviot Place, Edinburgh, EH8 9AG, UK.

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