Hassanzad Maryam, Orooji Azam, Valinejadi Ali, Velayati Aliakbar
M.D., Associate Professor, Pediatric Respiratory Disease Research Center, National Research Institute of Tuberculosis and Lung Diseases (NRITLD), Masih Daneshvari Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Ph.D. Candidate of Medical Informatics, Department of Health Information Management and Technology, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran.
Electron Physician. 2017 Dec 25;9(12):5974-5984. doi: 10.19082/5974. eCollection 2017 Dec.
Finding a valid diagnosis is mostly a prolonged process. Current advances in the sector of artificial intelligence have led to the appearance of expert systems that enrich the experiences and capabilities of doctors for making decisions for their patients.
The objective of this research was developing a fuzzy expert system for diagnosing Cystic Fibrosis (CF).
Defining the risk factors and then, designing the fuzzy expert system for diagnosis of CF were carried out in this cross-sectional study. To evaluate the performance of the proposed system, a dataset that corresponded to 70 patients with respiratory disease who were serially admitted to the CF Clinic in the Pediatric Respiratory Diseases Center, Masih Daneshvari Hospital in Tehran, Iran during August 2016 to January 2017 was considered. Whole procedures of system construction were implemented in a MATLAB environment.
Results showed that the suggested system can be used as a strong diagnostic tool with 93.02% precision, 89.29% specificity, 95.24% sensitivity and 92.86% accuracy for diagnosing CF. There was also a good relationship between the user and the system through the appealing user interface.
The system is equipped with information, knowledge, and expertise from certified specialists; hence, as a training tool it can be useful for new physicians. It is worth mentioning that the accomplishment of this project depends on advocacy of decision making in CF diagnosis. Nevertheless, it is expected that the system will reduce the number of false positives and false negatives in unusual cases.
找到一个有效的诊断通常是一个漫长的过程。人工智能领域的当前进展已导致专家系统的出现，这些系统丰富了医生为患者做出决策的经验和能力。
本研究的目的是开发一个用于诊断囊性纤维化（CF）的模糊专家系统。
在这项横断面研究中，先确定风险因素，然后设计用于CF诊断的模糊专家系统。为评估所提出系统的性能，考虑了一个数据集，该数据集对应于2016年8月至2017年1月期间连续入住伊朗德黑兰马西赫·丹什瓦里医院儿科呼吸疾病中心CF诊所的70例呼吸系统疾病患者。系统构建的整个过程在MATLAB环境中实现。
结果表明，所建议的系统可作为一种强大的诊断工具，用于诊断CF时的精确度为93.02%，特异性为89.29%，灵敏度为95.24%，准确率为92.86%。通过吸引人的用户界面，用户与系统之间也存在良好的关系。
该系统配备了来自认证专家的信息、知识和专业技能；因此，作为一种培训工具，它对新医生可能有用。值得一提的是，该项目的完成取决于CF诊断中决策的支持。然而，预计该系统将减少异常病例中的假阳性和假阴性数量。
