Nopour Raoof, Shanbehzadeh Mostafa, Kazemi-Arpanahi Hadi
Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Ira.
Department of Health Information Technology, School of Paramedical, Ilam University of Medical Sciences, Ilam, Iran.
Med J Islam Repub Iran. 2021 Apr 3;35:44. doi: 10.47176/mjiri.35.44. eCollection 2021.
Colorectal Cancer (CRC) is the most prevalent digestive system- related cancer and has become one of the deadliest diseases worldwide. Given the poor prognosis of CRC, it is of great importance to make a more accurate prediction of this disease. Early CRC detection using computational technologies can significantly improve the overall survival possibility of patients. Hence this study was aimed to develop a fuzzy logic-based clinical decision support system (FL-based CDSS) for the detection of CRC patients. This study was conducted in 2020 using the data related to CRC and non-CRC patients, which included the 1162 cases in the Masoud internal clinic, Tehran, Iran. The chi-square method was used to determine the most important risk factors in predicting CRC. Furthermore, the C4.5 decision tree was used to extract the rules. Finally, the FL-based CDSS was designed in a MATLAB environment and its performance was evaluated by a confusion matrix. Eleven features were selected as the most important factors. After fuzzification of the qualitative variables and evaluation of the decision support system (DSS) using the confusion matrix, the accuracy, specificity, and sensitivity of the system was yielded 0.96, 0.97, and 0.96, respectively. We concluded that developing the CDSS in this field can provide an earlier diagnosis of CRC, leading to a timely treatment, which could decrease the CRC mortality rate in the community.
结直肠癌（CRC）是最常见的消化系统相关癌症，已成为全球最致命的疾病之一。鉴于结直肠癌预后不佳，更准确地预测这种疾病非常重要。使用计算技术进行早期结直肠癌检测可显著提高患者的总体生存可能性。因此，本研究旨在开发一种基于模糊逻辑的临床决策支持系统（FL - CDSS）用于检测结直肠癌患者。本研究于2020年使用与结直肠癌和非结直肠癌患者相关的数据进行，这些数据包括伊朗德黑兰马苏德内部诊所的1162例病例。采用卡方检验方法确定预测结直肠癌的最重要风险因素。此外，使用C4.5决策树提取规则。最后，在MATLAB环境中设计基于FL的CDSS，并通过混淆矩阵评估其性能。选择了11个特征作为最重要的因素。对定性变量进行模糊化处理并使用混淆矩阵评估决策支持系统（DSS）后，该系统的准确率、特异性和灵敏度分别为0.96、0.97和0.96。我们得出结论，在该领域开发CDSS可以提供结直肠癌的早期诊断，从而实现及时治疗，这可能会降低社区中结直肠癌的死亡率。
