A Novel Machine Learning Algorithm to Predict the Lewy Body Dementias
A Novel Machine Learning Algorithm to Predict the Lewy Body Dementias
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.
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.
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