2026-10-13T13:03:03Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33993226 2026-09-25T05:36:06Z category_15 portal_316 item_type_3 month_year_09_2026

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 Edoardo Guido Torrigiani (25107823) Davide Cianca (25107826) Lorenzo Gaetani (8461842) Giovanna Nardi (25107829) Federico Paolini Paoletti (10502069) Davide Chiasserini (432117) Lucilla Parnetti (120805) Luca Gammaitoni (25107832) Igor Neri (25107835) Giovanni Bellomo (3342908) Neuroscience Alzheimer’s disease blood-based biomarkers cutoff definition machine learning plasma biomarkers supervised classification unsupervised clustering Introduction<p>Blood-based biomarkers (BBMs) are promising tools to facilitate biological diagnosis of Alzheimer’s Disease (AD). 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. The best supervised ML models achieved accuracies up to 0.88 in 10-fold cross-validation.</p>Discussion<p>Our results prove that clustering approaches can help in identifying reliable BBMs cutoffs for AD diagnosis even in the absence of CSF gold standard reference and that ML approaches combining multiple BBMs can strongly enhance their diagnostic accuracy.</p> 2026-09-25T05:36:06Z Dataset Dataset 10.3389/fnagi.2026.1922577.s001 https://figshare.com/articles/dataset/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/33993226 CC BY 4.0