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.
由于应用这种算法的领域(如医学)的敏感性，通常需要一种准确的推理算法。大多数医学诊断推理算法要么受到技术限制，要么受到准确性和效率的限制。即使是被认为是一种更近似的推理算法的选择和测试(ST)算法也受到其使用二部图建模领域知识和通过使用正交向量投影进行推理的方法的限制，以估计临床决策阶段(归纳)的诊断可能性。二部图知识库在概念上缺乏n元谓词的使用，而正交向量投影在推理过程中具有较高的计算量。本文的目的是为了提高ST算法的性能和准确性。首先，我们提出使用本体和基于语义web的规则进行知识表示，为推理提供支持。此外，本文还对ST算法进行了三个主要改进，以帮助改进其近似性。其次，我们设计了一个推理过程来实现与前面提到的知识库的交互。再次，我们将Hill’s因果准则模型引入ST的临床决策阶段，以克服正交向量投影的局限性。最后，改进的ST算法主要使用集合表示法(尽管实现为链表和队列)和数学表示法来表示和描述。改进后的ST算法在Wisconsin Breast Cancer Database (WBCD)和Wisconsin Diagnostic Breast Cancer (WDBC)数据集中的敏感性分别为0.81和0.89，特异性分别为0.82和1.0。此外，该算法在Wisconsin Breast Cancer Database (WBCD)和Wisconsin Diagnostic Breast Cancer (WDBC)数据集上的准确率分别为86.0%和88.72%。由于在增强的系统中添加了推理过程和本体解析任务，因此在较慢的时间内获得了准确性的提高。然而，结果系统的准确性和推理能力有了提高。
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