Harnessing topological machine learning in Raman spectroscopy: Perspectives for Alzheimer’s disease detection via cerebrospinal fluid analysis

The cerebrospinal fluid of 21 subjects who received a clinical diagnosis of Alzheimer’s disease (AD) as well as of 22 pathological controls has been collected and analysed by Raman spectroscopy (RS). We investigated whether the Raman spectra could be used to distinguish AD from controls, after a preprocessing procedure. We applied machine learning to a set of topological descriptors extracted from the spectra, achieving a high classification accuracy of 86%. Our experimentation indicates that RS and topological analysis may be a reliable and effective combination to confirm or disprove a clinical diagnosis of Alzheimer’s disease. The following steps will aim at leveraging the intrinsic interpretability of the topological data analysis to characterize the AD subtypes, e.g. by identifying the bands of the Raman spectrum relevant for AD detection, possibly increasing and/or confirming the knowledge about the precise molecular events and biological pathways behind the Alzheimer’s disease. • Introduce Topological Machine Learning (TML) for analysis of Raman Spectroscopy data. • Prove TML validity in Alzheimer’s detection using cerebrospinal fluid analysis. • Show that the TML pipeline excells in Alzheimer’s detection, compared to classical and deep models.