A knowledge-based artificial neural network classifier for pulmonary embolism diagnosis.
This paper aims to demonstrate that knowledge-based hybrid learning algorithms are positioned to offer better performance in comparison with purely empirical machine learning algorithms for the automatic classification task associated with the diagnosis of a medical condition described as pulmonary embolism (PE). The main premise is that there exists substantial and significant specialized knowledge in the domain of PE, which can readily be leveraged for bootstrapping a knowledge-based hybrid classifier that employs both the explanation-based and the empirical learning. The modified prospective investigation of pulmonary embolism diagnosis (PIOPED) criteria, which represent the pre-eminent collective experiential knowledge base among nuclear radiologists as a diagnosis procedure for PE, are conveniently defined in terms of a set of if-then rules. As such, it lends itself to being captured into a knowledge base through instantiating a knowledge-based hybrid learning algorithm. This study shows the instantiation of a knowledge-based artificial neural network (KBANN) classifier through the modified PIOPED criteria for the diagnosis of PE. The development effort for the KBANN that captures the rule base associated with the PIOPED criteria as well as further refinement of the same rule base through highly specialized domain expertise is presented. Through a testing dataset generated with the help of nuclear radiologists, performance of the instantiated KBANN is profiled. Performances of a set of empirical machine learning algorithms, which are configured as classifiers and include the nai ve Bayes, the Bayesian Belief network, the multilayer perceptron neural network, the C4.5 decision tree algorithm, and two meta learners with boosting and bagging, are also profiled on the same dataset for the purpose of comparison with that of the KBANN. Simulation results indicate that the KBANN can effectively model and leverage the PIOPED knowledge base and its further refinements through the domain expertise, and exhibited enhanced performance compared to those of purely empirical learning based classifiers.
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Department of Electrical Engineering and Computer Science, The University of Toledo, Toledo, OH 43606, USA. gserpen@eng.utoledo.edu
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D000465:Algorithms; D003198:Computer Simulation; D003663:Decision Trees; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016571:Neural Networks, Computer; D011336:Probability; D011655:Pulmonary Embolism / Q000175:diagnosis* / Q000000981:diagnostic imaging; D011877:Radionuclide Imaging / Q000379:methods; D012680:Sensitivity and Specificity; D014692:Ventilation-Perfusion Ratio