Authors:
Luigi Lella
1
;
Luana Gentile
2
;
Christian Pristipino
3
and
Danilo Toni
2
Affiliations:
1
ASUR Marche, via Oberdan n.2, Ancona, Italy
;
2
Dept. of Human Neurosciences, Sapienza University, Rome, Italy
;
3
San Filippo Neri Hospital, ASL1 Roma, Rome, Italy
Keyword(s):
Pattern Recognition and Machine Learning, Big Data in Healthcare, Data Mining and Data Analysis, Decision Support Systems.
Abstract:
Stroke patients discharge planning is a complex task that could be carried out by the use of a suitable decision support system. Such a platform should be based on unsupervised machine learning algorithms to reach the best results. More specifically, in this kind of prediction task clustering learning algorithms seem to perform better than the other unsupervised models. These algorithms are able to independently subdivide the treated clinical cases into groups, and they can serve to discover interesting correlations among the clinical variables taken into account and to improve the prediction accuracy of the treatment outcome. This work aims to compare the prediction accuracy of a particular clustering learning algorithm, the Growing Neural Gas, with the prediction accuracy of other supervised and unsupervised algorithms used in stroke patients discharge planning. This machine learning model is also able to accurately identify the input space topology. In other words it is characteri
zed by the ability to independently select a subset of attributes to be taken into consideration in order to correctly perform any predictive task.
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