Banihashem Seyed Yashar, Shishehchi Saman
Department Electrical and Computer Engineering, Buein Zahra Technical University, Buein Zahra, Qazvin, Iran.
Comput Methods Biomech Biomed Engin. 2023 May;26(6):639-649. doi: 10.1080/10255842.2022.2081502. Epub 2022 May 28.
Non-Alcohol Fatty liver disease is a common clinical complication. The paper aimed to develop a knowledge-based fatty liver detection system based on an ontology and detection rules extracted from a decision tree algorithm. Ontology is created to represent knowledge related to patients and fatty liver disease. By utilizing 43 SWRL rules and the Drool inference engine in ontology, we detected fatty liver patients. The training dataset size is 70% of clean data, including 580 electronic medical records of patients who suffer from liver diseases. After inferencing the rules, the number of patients who suffer from fatty liver disease in ontology is the same as the decision tree model. The paper validated the result generated by the ontology model through the results of the decision tree model.
非酒精性脂肪性肝病是一种常见的临床并发症。本文旨在基于本体和从决策树算法中提取的检测规则开发一个基于知识的脂肪肝检测系统。创建本体以表示与患者和脂肪性肝病相关的知识。通过在本体中使用43条SWRL规则和Drool推理引擎，我们检测出了脂肪肝患者。训练数据集大小为70%的干净数据，包括580例肝病患者的电子病历。在对规则进行推理后，本体中患有脂肪性肝病的患者数量与决策树模型相同。本文通过决策树模型的结果验证了本体模型生成的结果。
