Information System and Security and Countermeasures Experiments Center, Beijing Institute of Technology, Beijing, 100081, People's Republic of China.
Sci Rep. 2022 Jun 14;12(1):9858. doi: 10.1038/s41598-022-14143-8.
Due to concealed initial symptoms, many diabetic patients are not diagnosed in time, which delays treatment. Machine learning methods have been applied to increase the diagnosis rate, but most of them are black boxes lacking interpretability. Rule extraction is usually used to turn on the black box. As the number of diabetic patients is far less than that of healthy people, the rules obtained by the existing rule extraction methods tend to identify healthy people rather than diabetic patients. To address the problem, a method for extracting reduced rules based on biased random forest and fuzzy support vector machine is proposed. Biased random forest uses the k-nearest neighbor (k-NN) algorithm to identify critical samples and generates more trees that tend to diagnose diabetes based on critical samples to improve the tendency of the generated rules for diabetic patients. In addition, the conditions and rules are reduced based on the error rate and coverage rate to enhance interpretability. Experiments on the Diabetes Medical Examination Data collected by Beijing Hospital (DMED-BH) dataset demonstrate that the proposed approach has outstanding results (MCC = 0.8802) when the rules are similar in number. Moreover, experiments on the Pima Indian Diabetes (PID) and China Health and Nutrition Survey (CHNS) datasets prove the generalization of the proposed method.
由于隐匿性的初始症状，许多糖尿病患者不能被及时诊断，导致治疗延误。机器学习方法已被应用于提高诊断率，但大多数方法都是缺乏可解释性的“黑箱”。规则提取通常用于打开“黑箱”。由于糖尿病患者的数量远远少于健康人的数量，现有规则提取方法获得的规则往往倾向于识别健康人，而不是糖尿病患者。针对这一问题，提出了一种基于有偏随机森林和模糊支持向量机的简化规则提取方法。有偏随机森林使用 k-最近邻 (k-NN) 算法识别关键样本，并根据关键样本生成更多倾向于诊断糖尿病的树，以提高生成的糖尿病患者规则的倾向性。此外，基于错误率和覆盖率来降低条件和规则，以增强可解释性。基于北京医院采集的糖尿病体检数据（DMED-BH）数据集的实验表明，当规则数量相同时，所提出的方法具有出色的结果（MCC=0.8802）。此外，在 Pima Indian Diabetes（PID）和中国健康与营养调查（CHNS）数据集上的实验证明了所提出方法的泛化能力。
