Modern medicinal analysis is a complex procedure, requiring precise patient data, scientific knowledge obtained over numerous years and a theoretical understanding of related medical literature. To improve the accuracy and to reduce the time for diagnosis, clinical decision support systems (DSS) were introduced, which incorporate data mining schemes for enhancing the disease diagnosing accuracy. This work proposes a new disease-predicting model that involves 3 stages. Initially, “improved stemming and tokenization” are carried out in the pre-processing stage. Then, the “Fuzzy ontology, improved mutual information (MI), and correlation features” are extracted. Then, prediction is carried out via ensemble classifiers that include “improved Fuzzy logic, Long Short Term Memory (LSTM), Deep Convolution Neural Network (DCNN), and Bidirectional Gated Recurrent Unit (Bi-GRU)”.The outcomes from improved fuzzy logic, LSTM, and DCNN are further classified via Bi-GRU which offers the results. Specifically, Bi-GRU weights are optimally tuned using Deer Hunting Update Explored Arithmetic Optimization (DHUEAO). Finally, the efficiency of the proposed work is determined concerning a variety of metrics.

中文翻译：

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基于模糊本体的知识驱动疾病风险水平预测与优化辅助集成分类器

现代医学分析是一个复杂的过程，需要精确的患者数据、多年获得的科学知识以及对相关医学文献的理论理解。为了提高诊断的准确性并减少诊断时间，引入了临床决策支持系统（DSS），该系统结合了数据挖掘方案以提高疾病诊断的准确性。这项工作提出了一种新的疾病预测模型，涉及三个阶段。最初，“改进的词干提取和标记化”是在预处理阶段进行的。然后，提取“模糊本体、改进的互信息（MI）和相关特征”。然后，通过集成分类器进行预测，其中包括“改进的模糊逻辑、长短期记忆（LSTM）、深度卷积神经网络（DCNN）和双向门控循环单元（Bi-GRU）”。改进的模糊逻辑的结果、LSTM 和 DCNN 通过 Bi-GRU 进一步分类，Bi-GRU 提供结果。具体来说，Bi-GRU 权重使用猎鹿更新探索算术优化 (DHUEAO) 进行优化调整。最后，根据各种指标确定拟议工作的效率。