An expanded knowledge-graph model, CACTUS, classifies five AMD stages from non-imaging data with balanced accuracy of 0.27 to 0.34, outperforming standard ML baselines on the EYE-RISK last-visit dataset.
CACTUS: a Comprehensive Abstraction and Classification Tool for Uncovering Structures
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abstract
The availability of large data sets is providing an impetus for driving current artificial intelligent developments. There are, however, challenges for developing solutions with small data sets due to practical and cost-effective deployment and the opacity of deep learning models. The Comprehensive Abstraction and Classification Tool for Uncovering Structures called CACTUS is presented for improved secure analytics by effectively employing explainable artificial intelligence. It provides additional support for categorical attributes, preserving their original meaning, optimising memory usage, and speeding up the computation through parallelisation. It shows to the user the frequency of the attributes in each class and ranks them by their discriminative power. Its performance is assessed by application to the Wisconsin diagnostic breast cancer and Thyroid0387 data sets.
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CACTUS as a Reliable Tool for Early Classification of Age-related Macular Degeneration
An expanded knowledge-graph model, CACTUS, classifies five AMD stages from non-imaging data with balanced accuracy of 0.27 to 0.34, outperforming standard ML baselines on the EYE-RISK last-visit dataset.