A Novel Machine Learning Algorithm to Predict the Lewy Body Dementias

  • STATUS
    Recruiting
  • participants needed
    86
  • sponsor
    National and Kapodistrian University of Athens
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Updated on 16 February 2024
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dementia
movement disorder
lewy body disease

Summary

Parkinson's disease dementia (PDD) and Dementia with lewy bodies (DLB) are dementia syndromes that overlap in many clinical features, making their diagnosis difficult in clinical practice, particularly in advanced stages. We propose a machine learning algorithm, based only on non-invasively and easily in-the-clinic collectable predictors, to identify these disorders with a high prognostic performance.

Description

The algorithm will be develop using dataset from two specialized memory centers, employing a sample of PDD and DLB subjects whose diagnostic follow-up is available for at least 3 years after the baseline assessment. A restricted set of information regarding clinico- demographic characteristics, 6 neuropsychological tests (mini mental, PD Cognitive Rating Scale, Brief Visuospatial Memory test, Symbol digit written, Wechsler adult intelligence scale, trail making A and B) was used as predictors. Two classification algorithms, logistic regression and K-Nearest Neighbors (K-NNs), will be investigated for their ability to predict successfully whether patients suffered from PDD or DLB.

Details
Condition Dementia, Dementia, Alzheimer's Disease, Alzheimer's Disease
Age 50years - 90years
Treatment machine learning model
Clinical Study IdentifierNCT04448340
SponsorNational and Kapodistrian University of Athens
Last Modified on16 February 2024

Eligibility

 Yes No Not Sure

Inclusion Criteria

the PDD group comprised of patients fulfilling the Criteria for probable PDD
of the Movement Disorders Society (b) the DLB group comprised of patients
according to the recent revised criteria for probable DLB

Exclusion Criteria

major psychiatrics disorders, depression
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