Alzheimer Disease Detection from Raman Spectroscopy of the Cerebrospinal
  Fluid via Topological Machine Learning
Francesco Conti Martina Banchelli Valentina Bessi Cristina Cecchi Fabrizio Chiti Sara Colantonio Cristiano D'Andrea Marella de Angelis Davide Moroni Benedetta Nacmias Maria Antonietta Pascali Sandro Sorbi Paolo Matteini
The cerebrospinal fluid (CSF) of 19 subjects who received a clinical
diagnosis of Alzheimer's disease (AD) as well as of 5 pathological controls
have been collected and analysed by Raman spectroscopy (RS). We investigated
whether the raw and preprocessed Raman spectra could be used to distinguish AD
from controls. First, we applied standard Machine Learning (ML) methods
obtaining unsatisfactory results. Then, we applied ML to a set of topological
descriptors extracted from raw spectra, achieving a very good classification
accuracy (>87%). Although our results are preliminary, they indicate that RS
and topological analysis together may provide an effective combination to
confirm or disprove a clinical diagnosis of AD. The next steps will include
enlarging the dataset of CSF samples to validate the proposed method better
and, possibly, to understand if topological data analysis could support the
characterization of AD subtypes.