scholarly journals Comparative Study of Classification Algorithms for Early Identification of Parkinson’s Disease Based on Baseline Speech Features

2020 ◽  
pp. 186-189
Author(s):  
Santhosh Kumar C ◽  
Vishnu Kumar Kaliappan ◽  
Rajasekaran Thangaraj ◽  
Pandiyan P
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Feature Selection ◽  
Random Forest ◽  
Comparative Study ◽  
Early Identification ◽  
Naive Bayes ◽  
Classification Algorithms ◽  
Early Prediction ◽  
Speech Features

- In recent years, there is need for early identification of Parkinson’s disease (PD) based on measuring the features that causes disorders in elderly people. Around 80% of Parkinson’s patients show signs of speech-based disorders in the early stages of the disorder. In this paper, early prediction of Parkinson’s disease based on machine learning is compared between different classification algorithms. The proposed comparative study composed of feature extraction, preprocessing, feature selection and three different classification processes. Baseline features and Iterative Feature selection methods were well thought-out for feature selection process. We compare the performance of classification algorithms used for early prediction of Parkinson’s patients with speech disorders. Naïve Bayes, Multilayer Perceptron, Random Forest and J48 Classification algorithms were used for the categorization of Parkinson's patients in the experimental study. Random Forest and Naïve Bayes classification shows better performance from other two classifiers. 94.1176 % accuracy was obtained from the PD dataset with the smaller number of speech features.

Development of a Stacking-Based Ensemble Machine Learning for Detection of Depression in Parkinson’s Disease: Preliminary Research

2021 ◽  
Vol 9 (1) ◽  
pp. 5
Author(s):  
Haewon Byeon
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Random Forest ◽  
Naive Bayes ◽  
Depression Scale ◽  
Prognostic Index ◽  
Predictive Performance ◽  
Naïve Bayes ◽  
Outcome Variable

This preliminary study used the stacking ensemble to explore the major elements (factors) which could predict depression in patients with Parkinson’s disease and presented baseline data for developing a nomogram prognostic index for predicting high-risk groups for depression among patients with Parkinson’s disease in the future. Depression, an outcome variable, was divided into “with depression” and “without depression” using the Geriatric Depression Scale-30 (GDS-30). This study developed nine machine learning models (ANN, random forest, naive bayes, CART, ANN+LR, random forest+LR, naive bayes+LR, CART+LR, and random forest+naive bayes+CART+ANN+LR). The predictive performance (e.g., REMS, IA, Ev) of each machine learning model was validated through 10-fold cross-validation. The analysis results showed that the random forest+LR had the best predictive performance: RMSE = 0.16, IA = 0.73, and Ev = 0.48. This study analyzed the normalized importance of the random forest+LR model’s variables (the final model) and confirmed that K-MMSE, K-MoCA, Global CDR, sum of boxes in CDR, total score of UPDRS, motor score of UPDRS, K-IADL, H and Y staging, Schwab and England ADL, and REM and RBD were ten major variables with high weight among predictors of Parkinson’s disease with depression in South Korea. It is necessary as well to develop interpretable machine learning to build a model for predicting depression in patients with Parkinson’s disease that can be used in the medical field.

scholarly journals Improving Medication Regimen Recommendation for Parkinson’s Disease Using Sensor Technology

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3553
Author(s):  
Jeremy Watts ◽  
Anahita Khojandi ◽  
Rama Vasudevan ◽  
Fatta B. Nahab ◽  
Ritesh A. Ramdhani
Keyword(s):  
Parkinson’S Disease ◽  
Time Series ◽  
Parkinson's Disease ◽  
Random Forest ◽  
Treatment Planning ◽  
Time Series Data ◽  
Classification Model ◽  
Series Data ◽  
Demographic Information ◽  
Subjective Data

Parkinson’s disease medication treatment planning is generally based on subjective data obtained through clinical, physician-patient interactions. The Personal KinetiGraph™ (PKG) and similar wearable sensors have shown promise in enabling objective, continuous remote health monitoring for Parkinson’s patients. In this proof-of-concept study, we propose to use objective sensor data from the PKG and apply machine learning to cluster patients based on levodopa regimens and response. The resulting clusters are then used to enhance treatment planning by providing improved initial treatment estimates to supplement a physician’s initial assessment. We apply k-means clustering to a dataset of within-subject Parkinson’s medication changes—clinically assessed by the MDS-Unified Parkinson’s Disease Rating Scale-III (MDS-UPDRS-III) and the PKG sensor for movement staging. A random forest classification model was then used to predict patients’ cluster allocation based on their respective demographic information, MDS-UPDRS-III scores, and PKG time-series data. Clinically relevant clusters were partitioned by levodopa dose, medication administration frequency, and total levodopa equivalent daily dose—with the PKG providing similar symptomatic assessments to physician MDS-UPDRS-III scores. A random forest classifier trained on demographic information, MDS-UPDRS-III scores, and PKG time-series data was able to accurately classify subjects of the two most demographically similar clusters with an accuracy of 86.9%, an F1 score of 90.7%, and an AUC of 0.871. A model that relied solely on demographic information and PKG time-series data provided the next best performance with an accuracy of 83.8%, an F1 score of 88.5%, and an AUC of 0.831, hence further enabling fully remote assessments. These computational methods demonstrate the feasibility of using sensor-based data to cluster patients based on their medication responses with further potential to assist with medication recommendations.

scholarly journals An Early Prediction of Parkinson’s Disease Using Facial Emotional Recognition

2021 ◽  
Vol 1937 (1) ◽  
pp. 012058
Author(s):  
U Anusri ◽  
G Dhatchayani ◽  
Y Princely Angelinal ◽  
S. Kamalraj
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Early Prediction ◽  
Emotional Recognition
Sign in / Sign up

Export Citation Format

Share Document