Objective: Our aim was to develop a machine learning algorithm based only on non-invasively clinic collectable predictors, for the accurate diagnosis of these disorders. Methods: This is an ongoing prospective cohort study ( ClinicalTrials.gov identifier NCT number NCT04448340) of 78 PDD and 62 DLB subjects whose diagnostic follow-up is available for at least 3 years after the baseline assessment. We used predictors such as 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). We investigated logistic regression, K-Nearest Neighbors (K-NNs) Support Vector Machine (SVM), Naïve Bayes classifier, and Ensemble Model for their ability to predict successfully PDD or DLB diagnosis. Results: The K-NN classification model had an accuracy 91.2% of overall cases based on 15 best clinical and cognitive scores achieving 96.42% sensitivity and 81% specificity on discriminating between DLB and PDD. The binomial logistic regression classification model achieved an accuracy of 87.5% based on 15 best features, showing 93.93% sensitivity and 87% specificity. The SVM classification model had an accuracy 84.6% of overall cases based on 15 best features achieving 90.62% sensitivity and 78.58% specificity. A model created on Naïve Bayes classification had 82.05% accuracy, 93.10% sensitivity and 74.41% specificity. Finally, an Ensemble model, synthesized by the individual ones, achieved 89.74% accuracy, 93.75% sensitivity and 85.73% specificity. Conclusion: Machine learning method predicted with high accuracy, sensitivity and specificity PDD or DLB diagnosis based on non-invasively and easily in-the-clinic and neuropsychological tests.
the clinical range of post-coronavirus disease 2019 symptoms in patients with Parkinson's disease (PD) has not yet been thoroughly characterized, with the exception of a few small case studies. the aim of the present study was to investigate the motor and non-motor progression of patients with PD (PWP) and post-CoVID-19 syndrome (PCS) at baseline and at 6 months after infection with CoVID-19. a cross-sectional prospective study of 38 PWP+/PCS+ and 20 PWP+/PCS-matched for age, sex and disease duration was conducted. all patients were assessed at baseline and at 6 months using a structured clinicodemographic questionnaire, the Unified Parkinson's Disease Rating Scale Part III (the UPDrS III), the Montreal Cognitive assessment, the Hoehn and Yahr scale, the Geriatric Depression Scale and the levodopa equivalent daily dose (lEDD). there was a statistically significant difference in the LEDD (P=0.039) and UPDRS III (P=0.001) at baseline and at 6 months after infection with CoVID-19 between the PWP with PCS groups. the most common non-motor PCS symptoms were anosmia/hyposmia, sore throat, dysgeusia and skin rashes. there was no statistically significant difference in demographics or specific scores between the two groups, indicating that no prognostic factor for PCS in PWP could be identified. The novelty of the present study is that it suggests the new onset of non-motor PCS symptoms of PWP with a mild to moderate stage.
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