Book chapter
Predicting Huntington’s Disease: Extreme Learning Machine with Missing Values
Proceedings of ELM-2016, pp.195-206
Proceedings in Adaptation, Learning and Optimization, Springer International Publishing
05/26/2017
DOI: 10.1007/978-3-319-57421-9_16
Abstract
Problems with incomplete data and missing values are common and important in real-world machine learning scenarios, yet often underrepresented in the research field. Particularly data related to healthcare tends to feature missing values which must be handled properly, and ignoring any incomplete samples is not an acceptable solution. The Extreme Learning Machine has demonstrated excellent performance in a variety of machine learning tasks, including situations with missing values. In this paper, we present an application to predict the onset of Huntington’s disease several years in advance based on data from MRI brain scans. Experimental results show that such prediction is indeed realistic with reasonable accuracy, provided the missing values are handled with care. In particular, Multiple Imputation ELM achieves exceptional prediction accuracy.
Details
- Title: Subtitle
- Predicting Huntington’s Disease: Extreme Learning Machine with Missing Values
- Creators
- Emil Eirola - Arcada University of Applied Sciences, Helsinki, FinlandAnton Akusok - Arcada University of Applied Sciences, Helsinki, FinlandKaj-Mikael Björk - Risklab at Arcada University of Applied Sciences, Helsinki, FinlandHans Johnson - Department of Electrical Engineering, The University of Iowa, Iowa City, USAAmaury Lendasse - Department of Mechanical and Industrial Engineering and The Iowa Informatics Initiative, The University of Iowa, Iowa City, USA
- Resource Type
- Book chapter
- Publication Details
- Proceedings of ELM-2016, pp.195-206
- Series
- Proceedings in Adaptation, Learning and Optimization
- DOI
- 10.1007/978-3-319-57421-9_16
- eISSN
- 2363-6092
- ISSN
- 2363-6084
- Publisher
- Springer International Publishing; Cham
- Language
- English
- Date published
- 05/26/2017
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Electrical and Computer Engineering; Psychiatry; Industrial and Systems Engineering; The Iowa Institute for Biomedical Imaging; The Iowa Initiative for Artificial Intelligence; Iowa Informatics Initiative
- Record Identifier
- 9984221730602771
Metrics
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