Predicting Huntington’s Disease: Extreme Learning Machine with Missing Values

Emil Eirola, Anton Akusok, Kaj-Mikael Björk, Hans Johnson, Amaury Lendasse

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

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.
Original languageEnglish
Title of host publicationProceedings of ELM-2016
Number of pages12
Place of PublicationCham
PublisherSpringer
Publication date26.05.2017
Pages195-206
ISBN (Print)978-3-319-57420-2
ISBN (Electronic)978-3-319-57421-9
DOIs
Publication statusPublished - 26.05.2017
MoE publication typeA4 Article in conference proceedings
Event2016 the 7th International Conference on Extreme Learning Machines (ELM) - Marina Bay Sands, Singapore
Duration: 13.12.201615.12.2016

Publication series

Name Proceedings in Adaptation, Learning and Optimization (PALO)
Volume9

Keywords

  • 512 Business and Management
  • Extreme learning machine
  • Missing values
  • Multiple imputation
  • Huntington’s disease
  • Prediction

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  • Cite this

    Eirola, E., Akusok, A., Björk, K-M., Johnson, H., & Lendasse, A. (2017). Predicting Huntington’s Disease: Extreme Learning Machine with Missing Values. In Proceedings of ELM-2016 (pp. 195-206 ). ( Proceedings in Adaptation, Learning and Optimization (PALO); Vol. 9). Springer. https://doi.org/10.1007/978-3-319-57421-9_16