Apart from the need for superior accuracy, healthcare applications of intelligent systems also demand the deployment of interpretable machine learning models which allow clinicians to interrogate and validate extracted medical knowledge. Fuzzy rule-based models are generally considered interpretable that are able to reflect the associations between medical conditions and associated symptoms, through the use of linguistic if-then statements. Systems built on top of fuzzy sets are of particular appealing to medical applications since they enable the tolerance of vague and imprecise concepts that are often embedded in medical entities such as symptom description and test results. They facilitate an approximate reasoning framework which mimics human reasoning and supports the linguistic delivery of medical expertise often expressed in statements such as ‘weight low’ or ‘glucose level high’ while describing symptoms. This paper proposes an approach by performing data-driven learning of accurate and interpretable fuzzy rule bases for clinical decision support. The approach starts with the generation of a crisp rule base through a decision tree learning mechanism, capable of capturing simple rule structures. The crisp rule base is then transformed into a fuzzy rule base, which forms the input to the framework of adaptive network-based fuzzy inference system (ANFIS), thereby further optimising the parameters of both rule antecedents and consequents. Experimental studies on popular medical data benchmarks demonstrate that the proposed work is able to learn compact rule bases involving simple rule antecedents, with statistically better or comparable performance to those achieved by state-of-the-art fuzzy classifiers.

除了需要卓越的准确性外，智能系统的医疗保健应用还需要部署可解释的机器学习模型，使临床医生能够询问和验证提取的医学知识。基于模糊规则的模型通常被认为是可解释的，它能够通过使用语言 if-then 语句来反映医疗状况和相关症状之间的关联。建立在模糊集之上的系统对医学应用特别有吸引力，因为它们能够容忍通常嵌入在医学实体中的模糊和不精确的概念，例如症状描述和测试结果。它们有助于模拟人类推理的近似推理框架，并支持医学专业知识的语言传递，通常在描述症状时用“体重低”或“葡萄糖水平高”等语句表达。本文提出了一种通过对准确且可解释的模糊规则库进行数据驱动学习来支持临床决策的方法。该方法首先通过决策树学习机制生成清晰的规则库，能够捕获简单的规则结构。然后将清晰的规则库转换为模糊规则库，形成基于自适应网络的模糊推理系统 (ANFIS) 框架的输入，从而进一步优化规则前件和后件的参数。