title: Predicting probable Alzheimer's disease using linguistic deficits and biomarkers.
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.
  
  
  RESULTS
  Our models learned several syntactic, lexical, and n-gram linguistic biomarkers to distinguish the probable AD group from the healthy group. In contrast to the healthy group, we found that the probable AD patients had significantly less usage of syntactic components and significantly higher usage of lexical components in their language. Also, we observed a significant difference in the use of n-grams as the healthy group were able to identify and make sense of more objects in their n-grams than the probable AD group. As such, our best diagnostic model significantly distinguished the probable AD group from the healthy elderly group with a better Area Under the Receiving Operating Characteristics Curve (AUC) using the Support Vector Machines (SVM).
  
  
  CONCLUSIONS
  Experimental and statistical evaluations suggest that using ML algorithms for learning linguistic biomarkers from the verbal utterances of elderly individuals could help the clinical diagnosis of probable AD. We emphasise that the best ML model for predicting the disease group combines significant syntactic, lexical and top n-gram features. However, there is a need to train the diagnostic models on larger datasets, which could lead to a better AUC and clinical diagnosis of probable AD.
authors.affiliation: Intelligent Health Research Group, School of Information Technology, Monash University, Jalan Lagoon Selatan, Bandar Sunway, 47500, Malaysia. sylvester.orimaye@monash.edu.
authors.forename: Sylvester O
authors.initials: SO
authors.lastname: Orimaye
authors.affiliation: Intelligent Health Research Group, School of Information Technology, Monash University, Jalan Lagoon Selatan, Bandar Sunway, 47500, Malaysia.
authors.forename: Jojo S-M
authors.initials: JS
authors.lastname: Wong
authors.affiliation: Jeffrey Cheah School of Medicine and Health Sciences, Monash University, Jalan Lagoon Selatan, Bandar Sunway, 47500, Malaysia.
authors.forename: Karen J
authors.initials: KJ
authors.lastname: Golden
authors.affiliation: Jeffrey Cheah School of Medicine and Health Sciences, Monash University, Jalan Lagoon Selatan, Bandar Sunway, 47500, Malaysia.
authors.forename: Chee P
authors.initials: CP
authors.lastname: Wong
authors.affiliation: Centre for Medical Informatics, Usher Institute for Population Health Sciences & Informatics, The University of Edinburgh, Teviot Place, Edinburgh, EH8 9AG, UK.
authors.forename: Ireneous N
authors.initials: IN
authors.lastname: Soyiri
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D000544:Alzheimer Disease / Q000175:diagnosis* / Q000523:psychology; D015415:Biomarkers / Q000032:analysis; D006801:Humans; D008037:Linguistics; D000069550:Machine Learning; D008875:Middle Aged; D013060:Speech
chemical_list: D015415:Biomarkers
keywords: Alzheimer’s disease; Clinical diagnostics; Machine learning; Neurolinguistics; Prediction