Nopour Raoof
Department of Health Information Management, Student Research Committee, School of Health Management and Information Sciences Branch, Iran University of Medical Sciences, Tehran, Iran.
Heliyon. 2023 Nov 29;9(12):e22654. doi: 10.1016/j.heliyon.2023.e22654. eCollection 2023 Dec.
Considering the silent progression of esophageal cancer, the survival prediction of this disease is crucial in enhancing the quality of life of these patients globally. So far, no prediction solution has been introduced for the survival of EC in Iran based on the machine learning approach. So, this study aims to develop a prediction model for the five-year survival of EC based on the ML approach to promote clinical outcomes and various treatment and preventive plans.
In this retrospective study, we investigated the 1656 cases of survived and non-survived EC patients belonging to Imam Khomeini Hospital in Sari City from 2013 to 2020. The multivariable regression analysis was used to select the best predictors of five-year survival. We leveraged random forest, eXtreme Gradient Boosting, support vector machine, artificial neural networks, Bayesian networks, J-48 decision tree, and K-nearest neighborhood to develop the prediction models. To get the best model for predicting the five-year survival of EC, we compared them using the area under the receiver operator characteristics.
The age at diagnosis, body mass index, smoking, obstruction, dysphagia, weight loss, lymphadenopathy, chemotherapy, radiotherapy, family history of EC, tumor stage, type of appearance, histological type, grade of differentiation, tumor location, tumor size, lymphatic invasion, vascular invasion, and platelet albumin ratio were considered as the best predictors associated with the five-year survival of EC based on the regression analysis. In this respect, the random forest with the area under the receiver operator characteristics of 0.95 was identified as a superior model.
The experimental results of the current study showed that the random forest could have a significant role in enhancing the quality of care in EC patients by increasing the effectiveness of follow-up and treatment measures introduced by care providers.
鉴于食管癌的隐匿性进展，对该疾病的生存预测对于提高全球这些患者的生活质量至关重要。到目前为止，伊朗尚未基于机器学习方法引入针对食管癌生存情况的预测解决方案。因此，本研究旨在基于机器学习方法开发一种食管癌五年生存率的预测模型，以改善临床结果以及各种治疗和预防方案。
在这项回顾性研究中，我们调查了2013年至2020年期间来自萨里市伊玛目霍梅尼医院的1656例食管癌存活和非存活患者。采用多变量回归分析来选择五年生存率的最佳预测因素。我们利用随机森林、极端梯度提升、支持向量机、人工神经网络、贝叶斯网络、J-48决策树和K近邻算法来开发预测模型。为了获得预测食管癌五年生存率的最佳模型，我们使用受试者操作特征曲线下面积对它们进行比较。
基于回归分析，诊断时年龄、体重指数、吸烟、梗阻、吞咽困难、体重减轻、淋巴结病、化疗、放疗、食管癌家族史、肿瘤分期、外观类型、组织学类型、分化程度、肿瘤位置、肿瘤大小、淋巴浸润、血管浸润和血小板白蛋白比值被认为是与食管癌五年生存率相关的最佳预测因素。在这方面，受试者操作特征曲线下面积为0.95的随机森林被确定为 superior 模型。
本研究的实验结果表明，随机森林通过提高护理人员引入的随访和治疗措施的有效性，在提高食管癌患者的护理质量方面可能发挥重要作用。
