Machine learning ensemble for neurological disorders

Parkinson disease is a neurodegenerative disorder of the central nerve system which affects body movements. The proposed technique selects best five machine learning models competitively, out of 25 state-of-the-art regression models to generate a robust ensemble. Data from 42 patients having early s...

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Bibliographic Details
Published in:Neural computing & applications 2020-08, Vol.32 (16), p.12697-12714
Main Authors: Kaur Harkawalpreet, Malhi Avleen Kaur, Pannu, Husanbir Singh
Format: Article
Language:English
Subjects:
Correlation analysis
Machine learning
Neurological diseases
Parkinson's disease
Regression analysis
Regression models
Robustness (mathematics)
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Summary:Parkinson disease is a neurodegenerative disorder of the central nerve system which affects body movements. The proposed technique selects best five machine learning models competitively, out of 25 state-of-the-art regression models to generate a robust ensemble. Data from 42 patients having early stage of Parkinson disease were collected which contains a total of 5875 voice recordings. Numerous state-of-the-art machine learning models have been explored to predict the motor Unified Parkinson’s Disease Rating Score (UPDRS) for the collected voice measures. Evaluation parameters such as correlation, R-Square, RMSE, and accuracy have been calculated for comparative analysis. Results from the ensemble model consisting of best five models have been recalculated to analyze the prediction. K-fold validation has been incorporated to measure the robustness of ensembled model. The proposed ensemble yields UPDRS with higher accuracy of 99.6% making it well suitable to assist the diagnose for Parkinson disease.
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-020-04720-1