Neuropsychological tests and machine learning: identifying predictors of MCI and dementia progression

Topic: Risk, prevention and care in population studies

The tests qualitatively and statistically associated to a cognitive decline are MMSE, FAB, BSTR, AM, and VSF, of which at least three were considered the most informative also by machine learning. 73% average accuracy was obtained in the diagnosis prediction on three subsets of original and external data. Detecting the most informative tests could reduce the visits' time and prevent the cognitive assessment from being biased by external factors. Machine learning models' prediction represents a useful baseline for the clinician's actual diagnosis and a reliable insight into the future development of the patient's cognitive status.

Key findings

• The tests qualitatively and statistically associated to a cognitive decline are MMSE, FAB, BSTR, AM, and VSF, of which at least three were considered the most informative also by machine learning. 73% average accuracy was obtained in the diagnosis prediction on three subsets of original and external data.
• Detecting the most informative tests could reduce the visits' time and prevent the cognitive assessment from being biased by external factors.
• Machine learning models' prediction represents a useful baseline for the clinician's actual diagnosis and a reliable insight into the future development of the patient's cognitive status.

Why it matters

This study found that certain neuropsychological tests were associated with cognitive decline and that machine learning models predicted diagnosis with 73% average accuracy. It is about identifying informative tests and predicting progression, not about treating, curing or preventing Alzheimer's disease.

New to this subject? Start with our plain-English guide: Getting a diagnosis →

Study details

• Participants281
• Evidence strengthLow

Read the original

• DOI: 10.1007/s40520-025-02962-4
• PubMed: 40072711
• Original source →
• Free full text: Aging Clinical and Experimental Research →

Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).

@article{cazzolli2025-40072711,
  author  = {Cazzolli, Carlotta and Chierici, Marco and Dallabona, Monica and Guella, Chiara and Jurman, Giuseppe},
  title   = {{Neuropsychological tests and machine learning: identifying predictors of MCI and dementia progression}},
  journal = {Aging Clinical and Experimental Research},
  year    = {2025},
  doi     = {10.1007/s40520-025-02962-4},
  pmid    = {40072711},
  url     = {https://doi.org/10.1007/s40520-025-02962-4}
}

Studies this one cites

Referenced by this study and also in the alzhub library, so you can read them here.

• Predicting the progression of mild cognitive impairment using machine learning: A systematic, quantitative and critical review Medical image analysis · 2020-10-06 · usefulness score 70/100 · Digital health tools
• “Mini-mental state” Journal of Psychiatric Research · 1975-11-01 · usefulness score 55/100 · Cognitive tests and assessment
• Special Care Units and Traditional Care in Dementia: Relationship with Behavior, Cognition, Functional Status and Quality of Life - A Review Dementia and Geriatric Cognitive Disorders Extra · 2013-10-05 · usefulness score 51/100 · Caregiving
• Sensitivity and False Alarm Rate of a Fall Sensor in Long-Term Fall Detection in the Elderly Gerontology · 2014-08-13 · usefulness score 50/100 · Digital health tools
• Data mining methods in the prediction of Dementia: A real-data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random forests BMC research notes · 2011-08-17 · usefulness score 47/100 · Digital health tools
• Mild cognitive impairment: current research and clinical implications Seminars in neurology · 2007-02-01 · usefulness score 45/100 · Mild cognitive impairment
• Older Adults' Reasons for Using Technology while Aging in Place Gerontology · 2015-06-04 · usefulness score 39/100 · Digital health tools

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• Baseline MRI predictors of conversion from MCI to probable AD in the ADNI cohort Current Alzheimer research · 2009-08-01 · usefulness score 66/100

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