Feature selection based on information theory in the clock drawing test

Bennasar, Mohamed, Setchi, Rossitza ORCID: https://orcid.org/0000-0002-7207-6544, Bayer, Antony James ORCID: https://orcid.org/0000-0002-7514-248X and Hicks, Yulia Alexandrovna ORCID: https://orcid.org/0000-0002-7179-4587 2013. Feature selection based on information theory in the clock drawing test. Presented at: 17th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems, Kitakyushu, Japan, 9 -12 September 2013. Procedia Computer Science. , vol.22 pp. 902-911. 10.1016/j.procs.2013.09.173

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Abstract

The Clock Drawing Test is one of the most widely used screening tools for cognitive impairment and dementia. Since its introduction, more than fifteen scoring systems have been developed to assess the clock drawings. However, very little research has been conducted to study the significance of the elements (features) of the clock drawings for the correct diagnosis of dementia. This paper employs a feature selection method called Feature Interaction Maximization (FIM) to identify the most significant visual features of the test, which can be associated with dementia. The proposed approach is tested with a dataset of 648 clock drawings produced by dementia patients and healthy individuals. The results are compared with other methods used by medical experts. Furthermore, the paper compares the FIM method with an alternative feature selection method based on Information Gain. The results show that the FIM method selects features with higher discriminative power which leads to a deeper understanding of the Clock Drawing Test.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Publication
Status:	Published
Schools:	Engineering Medicine
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > T Technology (General) Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4050 Electronic information resources
Uncontrolled Keywords:	Clock Drawing Test; Feature Selection; Information Theory; Interaction Information
Related URLs:	Organisation
Last Modified:	06 Jul 2023 10:18
URI:	https://orca.cardiff.ac.uk/id/eprint/47601

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