Modeling obesity using abductive networks
Computers and Biomedical Research
Concise, intelligible, and approximate profiling of multiple classes
International Journal of Human-Computer Studies - Special issue on Machine Discovery
IEEE Transactions on Information Technology in Biomedicine
Artificial Intelligence in Medicine
Parallel growing and training of neural networks using output parallelism
IEEE Transactions on Neural Networks
On the construction and training of reformulated radial basis function neural networks
IEEE Transactions on Neural Networks
Efficient classification for multiclass problems using modular neural networks
IEEE Transactions on Neural Networks
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Differential diagnosis of multiple disorders is a challenging problem in clinical medicine. According to the divide-and-conquer principle, this problem can be handled more effectively through decomposing it into a number of simpler sub-problems, each solved separately. We demonstrate the advantages of this approach using abductive network classifiers on the 6-class standard dermatology dataset. Three problem decomposition scenarios are investigated, including class decomposition and two hierarchical approaches based on clinical practice and class separability properties. Two-stage classification schemes based on hierarchical decomposition boost the classification accuracy from 91% for the single-classifier monolithic approach to 99%, matching the theoretical upper limit reported in the literature for the accuracy of classifying the dataset. Such models are also simpler, achieving up to 47% reduction in the number of input variables required, thus reducing the cost and improving the convenience of performing the medical diagnostic tests required. Automatic selection of only relevant inputs by the simpler abductive network models synthesized provides greater insight into the diagnosis problem and the diagnostic value of various disease markers. The problem decomposition approach helps plan more efficient diagnostic tests and provides improved support for the decision-making process. Findings are compared with established guidelines of clinical practice, results of data analysis, and outcomes of previous informatics-based studies on the dataset.