A natural language processing solution to probable Alzheimer’s disease detection in conversation transcripts

  • Federica Comuni

Student thesis: Bachelor

Abstract

This study proposes an accuracy comparison of two of the best performing machine learning algorithms in natural language processing, the Bayesian Network and the Long Short-Term Memory (LSTM) Recurrent Neural Network, in detecting Alzheimer’s disease symptoms in conversation transcripts. Because of the current global rise of life expectancy, the number of seniors affected by Alzheimer’s disease worldwide is increasing each year. Early detection is important to ensure that affected seniors take measures to relieve symptoms when possible or prepare plans before further cognitive decline occurs. Literature shows that natural language processing can be a valid tool for early diagnosis of the disease. This study found that mild dementia and possible Alzheimer’s can be detected in conversation transcripts with promising results, and that the LSTM is particularly accurate in said detection, reaching an accuracy of 86.5% on the chosen dataset. The Bayesian Network classified with an accuracy of 72.1%. The study confirms the effectiveness of a natural language processing approach to detecting Alzheimer’s disease.

Date of Award2019-Aug-30
Original languageEnglish
SupervisorKamilla Klonowska (Supervisor) & Dawit Mengistu (Examiner)

Educational program

  • Bachelor programme in Computer Software Development

University credits

  • 15 HE credits

Swedish Standard Keywords

  • Computer Sciences (10201)

Keywords

  • bayesian network
  • long short-term memory recurrent neural network
  • machine learning
  • natural language processing
  • alzheimer's disease
  • early detection

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