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.
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Eirola, E., Akusok, A., Björk, KM., Johnson, H., Lendasse, A. (2018). Predicting Huntington’s Disease: Extreme Learning Machine with Missing Values. In: Cao, J., Cambria, E., Lendasse, A., Miche, Y., Vong, C. (eds) Proceedings of ELM-2016. Proceedings in Adaptation, Learning and Optimization, vol 9. Springer, Cham. https://doi.org/10.1007/978-3-319-57421-9_16
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