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Title: Hybrid approach to interpretable multiple classifier system for intelligent clinical decision support
Author: Zimit, Sani Ibrahim
Awarding Body: University of Reading
Current Institution: University of Reading
Date of Award: 2013
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Data-driven decision support approaches have been increasingly employed in recent years in order to unveil useful diagnostic and prognostic patterns from data accumulated in clinical repositories. Given the diverse amount of evidence generated through everyday clinical practice and the exponential growth in the number of parameters accumulated in the data, the capability of finding purposeful task-oriented patterns from patient records is crucial for providing effective healthcare delivery. The application of classification decision support tool in clinical settings has brought about formidable challenges that require a robust system. Knowledge Discovery in Database (KDD) provides a viable solution to decipher implicit knowledge in a given context. KDD classification techniques create models of the accumulated data according to induction algorithms. Despite the availability of numerous classification techniques, the accuracy and interpretability of the decision model are fundamental in the decision processes. Multiple Classifier Systems (MCS) based on the aggregation of individual classifiers usually achieve better decision accuracy. The down size of such models is due to their black box nature. Description of the clinical concepts that influence each decision outcome is fundamental in clinical settings. To overcome this deficiency, the use of artificial data is one technique advocated by researchers to extract an interpretable classifier that mimics the MCS. In the clinical context, practical utilisation of the mimetic procedure depends on the appropriateness of the data generation method to reflect the complexities of the evidence domain. A well-defined intelligent data generation method is required to unveil associations and dependency relationships between various entities the evidence domain. This thesis has devised an Interpretable Multiple classifier system (IMC) using the KDD process as the underlying platform. The approach integrates the flexibility of MCS, the robustness of Bayesian network (BN) and the concept of mimetic classifier to build an interpretable classification system. The BN provides a robust and a clinically accepted formalism to generate synthetic data based on encoded joint relationships of the evidence space. The practical applicability of the IMC was evaluated against the conventional approach for inducing an interpretable classifier on nine clinical domain problems. Results of statistical tests substantiated that the IMC model outperforms the direct approach in terms of decision accuracy.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available