A Novel Diagnosis System for Parkinson’s Disease Using K-means Clustering and Decision Tree

Parkinson’s disease (PD) among Alzheimer’s and epilepsy are one of the most common neurological disorders which appreciably affect not only live of patients but also their households. According to the current trend of aging social behaviour, it is expected to see a rise of Parkinson’s disease. Even though there is no cure for PD, a proper medication at the early stage can help significantly in alleviating the symptoms. Since, the traditional method for identifying PD is rather invasive, expansive and complicated for self-use, there is a high demand for using classification method on PD detection. This paper compares the performance of Neural Network and decision tree for classifying and discriminating healthy people for people with Parkinson’s disease (PD) by distinguishing dysphonia. The simulation results demonstrate that Neural Network outperformed decision tree by giving accurate results with 87% accuracy as compared to decision tree with only 84% accuracy in determining the classification of healthy and people with Parkinson’s.

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Automated and accurate assessment for postural abnormalities in patients with Parkinson’s disease based on Kinect and machine learning

Journal of NeuroEngineering and Rehabilitation ◽

10.1186/s12984-021-00959-4 ◽

2021 ◽

Vol 18 (1) ◽

Author(s):

Zhuoyu Zhang ◽

Ronghua Hong ◽

Ao Lin ◽

Xiaoyun Su ◽

Yue Jin ◽

...

Keyword(s):

Machine Learning ◽

Parkinson’S Disease ◽

Parkinson's Disease ◽

Decision Tree ◽

Medical Decision ◽

Accurate Assessment ◽

Decision Tree Classifier ◽

Tree Classifier ◽

Body Joints ◽

Postural Abnormalities

Abstract Background Automated and accurate assessment for postural abnormalities is necessary to monitor the clinical progress of Parkinson’s disease (PD). The combination of depth camera and machine learning makes this purpose possible. Methods Kinect was used to collect the postural images from 70 PD patients. The collected images were processed to extract three-dimensional body joints, which were then converted to two-dimensional body joints to obtain eight quantified coronal and sagittal features (F1-F8) of the trunk. The decision tree classifier was carried out over a data set established by the collected features and the corresponding doctors’ MDS-UPDRS-III 3.13 (the 13th item of the third part of Movement Disorder Society-Sponsored Revision of the Unified Parkinson’s Disease Rating Scale) scores. An objective function was implanted to further improve the human–machine consistency. Results The automated grading of postural abnormalities for PD patients was realized with only six selected features. The intraclass correlation coefficient (ICC) between the machine’s and doctors’ score was 0.940 (95%CI, 0.905–0.962), meaning the machine was highly consistent with the doctors’ judgement. Besides, the decision tree classifier performed outstandingly, reaching 90.0% of accuracy, 95.7% of specificity and 89.1% of sensitivity in rating postural severity. Conclusions We developed an intelligent evaluation system to provide accurate and automated assessment of trunk postural abnormalities in PD patients. This study demonstrates the practicability of our proposed method in the clinical scenario to help making the medical decision about PD.

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A Novel Diagnosis System for Parkinson’s Disease Using K-means Clustering and Decision Tree

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Automated and accurate assessment for postural abnormalities in patients with Parkinson’s disease based on Kinect and machine learning

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