Hierarchical neural networks for partial diagnosis in medicine

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abstract: AbstractVarious domains require hierarchical classification. In medicine, learning partial diagnoses can be helpful when time and information constraints are present. Hierarchical neural networks provide a good means to perform partial diagnosis. We implemented a hierarchical backpropagation-based model for the domain of thyroid diseases, and compared the results against those of nonhierarchical networks in terms of sensitivities and specificities. In our system, high-level neural networks filter instances that are relevant for use in specialized neural networks. The hierarchical model required fewer epochs tobe trained and yielded a higher classification rate in the test set than did the nonhierarchical one. The hierarchical model also had the advantage that fewer data attributes for each instance were required at higher levels. Therefore, using this model decreases the problem of dealing with missing values, and provides a framework to establish a parsimonious sequence of tests for diagnosing thyroid diseases.

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