ONCObc-ST: An Improved Clinical Reasoning Algorithm Based on Select and Test (ST) Algorithm for Diagnosing Breast Cancer
Olaide Nathaniel Oyelade and Sunday Adeyemi Adewuyi
Current Research in Bioinformatics
The need for an accurate reasoning algorithm is usually necessitated by the sensitivity of domain of (medicine as example) application of such algorithms. Most reasoning algorithms for medical diagnosis are either limited by their techniques or accuracy and efficiency. Even the Select and Test (ST) algorithm which is considered a more approximate reasoning algorithm is also limited by its approach of using bipartite graph in modeling domain knowledge and making inference through the use of orthogonal vector projection for estimating likelihood of diagnosis at the clinical decision stage (induction). While the bipartite graph knowledge base lacks n-ary use of predicate on concepts, orthogonal vector projection on the other hand has high computation for the inference process. The aim of this paper is to enhance ST algorithm for improved performance and accuracy. First, we propose the use of ontologies and semantic web based rule for knowledge representation so as to provide support for inference making. Furthermore, three major improvements were added to ST algorithm to aid the improvement of its approximation. Secondly, we designed an inference making procedure to enable interaction with the knowledge base mentioned earlier. Thirdly, we model Hill’s Criteria of Causation into clinical decision stage of ST to overcome the limitation of orthogonal vector projection. Lastly, the improved ST algorithm was largely represented and described using set notations (though implemented as linked-list and queues) and mathematical notations. The result of the improved ST algorithm revealed a sensitivity of 0.81 and 0.89 and specificity of 0.82 and 1.0 in the Wisconsin Breast Cancer Database (WBCD) and Wisconsin Diagnostic Breast Cancer (WDBC) datasets respectively. In addition, the accuracy obtained from the proposed algorithm was 86.0% and 88.72% for the Wisconsin Breast Cancer Database (WBCD) and Wisconsin Diagnostic Breast Cancer (WDBC) datasets respectively. This enhancement in accuracy was obtained at a slowdown time due to the reasoning process and ontology parsing task added to the enhanced system. However, there was an improvement in the accuracy and inference power of the resulting system.
© 2019 Olaide Nathaniel Oyelade and Sunday Adeyemi Adewuyi. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.