NCT04448340

A Novel Machine Learning Algorithm to Predict the Lewy Body Dementias

A Novel Machine Learning Algorithm to Predict the Lewy Body Dementias Using Clinical and Neuropsychological Scores

• Status: Unknown
• Phase: —
• Study type: Observational
• Enrollment: 200 (estimated)
• Sponsor: National and Kapodistrian University of Athens · Academic / Other
• Sex: All
• Age: 50 Years – 90 Years
• Healthy volunteers: Not accepted

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.

Detailed 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.

Conditions

• Dementia

Interventions

Type	Name	Description
DIAGNOSTIC_TEST	machine learning model	Two classification algorithms, logistic regression and K-Nearest Neighbors (K-NNs), were investigated for their ability to predict successfully whether patients suffered from PDD or DLB.

Timeline

• Start date: 2019-09-01
• Primary completion: 2020-10-01
• Completion: 2021-03-01
• First posted: 2020-06-25
• Last updated: 2020-09-10

Locations

1 site across 1 country: Greece