Evaluation of Source-wise Missing Data Techniques for the Prediction of Parkinson's Disease Using Smartphones

Multi-source datasets often present the challenge of source-wise missing data which can render large portions of the dataset inaccessible. The applicability of traditional missing data techniques on multi-source datasets is poorly understood. We present the first quantitative evaluation of the state...

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Vydáno v:Proceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) s. 3927 - 3930
Hlavní autoři: Prince, John, Andreotti, Fernando, De Vos, Maarten
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.05.2019
Témata:
Data models
Feature extraction
Logistics
multi-modal autoencoding
Parkinson's disease
sensor fusion
Smart phones
source-wise imputation
Source-wise missing data
Training
ISSN:2379-190X
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Shrnutí:Multi-source datasets often present the challenge of source-wise missing data which can render large portions of the dataset inaccessible. The applicability of traditional missing data techniques on multi-source datasets is poorly understood. We present the first quantitative evaluation of the state-of-the-art missing data techniques as applied to a freely available dataset of smart-phone recordings from Parkinsonian patients wherein source-wise missing data is simulated. The classification accuracy and imputation error of five missing data techniques, including a multi-modal autoencoder and multi-source ensemble learning, are compared at varying levels of missingness. These results demonstrate the relative applicability of each technique under different conditions and subsequently highlight the challenges of source-wise missing on remotely collected datasets. Specifically, multi-source ensemble learning proves to be a highly successful alternative to the traditional imputation techniques when a majority of observations possess missing data.
ISSN:2379-190X
DOI:10.1109/ICASSP.2019.8682660