Data Sheet 1_Application of unsupervised and supervised machine learning approaches to blood-based biomarkers of Alzheimer’s disease in a memory clinic cohort.docx

Establishing reliable cutoffs for BBMs represents a major issue for real-world implementation and the investigation of combined BBM approaches is limited. Our aim is to test different machine learning (ML) models for BBMs interpretation with respect to established gold-standard in vivo biomarkers, i.e., cerebrospinal fluid (CSF) biomarkers, to enhance their standalone and combined diagnostic value for AD.</p>Methods<p>We selected 434 patients who referred to Perugia University Hospital for perceived cognitive decline with available CSF A β 42/40, pTau181 and tTau and plasma Aβ42, Aβ40, pTau217, pTau181 and NfL.

Multiple unsupervised clustering algorithms for cutoff determination and supervised classification models were compared for the prediction of AD defined by a CSF A⁺/T ⁺ profile.</p>Results<p>Different unsupervised clustering models obtained cutoffs with high performances in predicting an AD CSF profile (accuracy > 0.85). Each combination of cutoffs was tested, the best ones achieving A⁺/T⁺ CSF profile prevalences > 0.9 in BBM-positive subgroups.