Abstract
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.
Highlights
Reasoning applies logic to a given algorithm to arrive at a goal or desired end
We have presented an enhanced and more approximate medical reasoning algorithm named ONCObc-Select and Test (ST) which was an improvement of ST algorithm
The improvement was achieved through the design of ontology-based knowledge representation that assists logical reasoning
Summary
Reasoning applies logic (rule application) to a given algorithm to arrive at a goal or desired end. Reasoning is to draw inferences appropriate to a given situation (Copeland, 2014). Clinical reasoning, which is a form of reasoning, is the process of reasoning through clinical findings over symptoms/manifestations presented by patient, with the aim of making clinical decision to identify an appropriate diagnosis. This reasoning process consists of the two stages of clinical diagnoses process (clinical findings and decision making) which when omitted in clinical reasoning algorithm design, can adversely affect the approximation and accuracy of the algorithm (Fernando and Henskens, 2013; Ramoni et al, 1992). In (Fernando and Henskens, 2016), the authors improved on (Fernando and Henskens, 2013) through the use of bipartite graph in modeling domain knowledge and making inference through the use of orthogonal vector projection for estimating likelihood of diagnosis in the clinical decision making process
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