Afrash Mohammad Reza, Shanbehzadeh Mostafa, Kazemi-Arpanahi Hadi
Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Department of Health Information Technology, School of Paramedical, Ilam University of Medical Sciences, Ilam, Iran.
Clin Med Insights Oncol. 2022 Aug 22;16:11795549221116833. doi: 10.1177/11795549221116833. eCollection 2022.
Gastric cancer remains one of the leading causes of worldwide cancer-specific deaths. Accurately predicting the survival likelihood of gastric cancer patients can inform caregivers to boost patient prognostication and choose the best possible treatment path. This study intends to develop an intelligent system based on machine learning (ML) algorithms for predicting the 5-year survival status in gastric cancer patients.
A data set that includes the records of 974 gastric cancer patients retrospectively was used. First, the most important predictors were recognized using the Boruta feature selection algorithm. Five classifiers, including J48 decision tree (DT), support vector machine (SVM) with radial basic function (RBF) kernel, bootstrap aggregating (Bagging), hist gradient boosting (HGB), and adaptive boosting (AdaBoost), were trained for predicting gastric cancer survival. The performance of the used techniques was evaluated with specificity, sensitivity, likelihood ratio, and total accuracy. Finally, the system was developed according to the best model.
The stage, position, and size of tumor were selected as the 3 top predictors for gastric cancer survival. Among the 6 selected ML algorithms, the HGB classifier with the mean accuracy, mean specificity, mean sensitivity, mean area under the curve, and mean F1-score of 88.37%, 86.24%, 89.72%, 88.11%, and 89.91%, respectively, gained the best performance.
The ML models can accurately predict the 5-year survival and potentially act as a customized recommender for decision-making in gastric cancer patients. The developed system in our study can improve the quality of treatment, patient safety, and survival rates; it may guide prescribing more personalized medicine.
胃癌仍然是全球癌症特异性死亡的主要原因之一。准确预测胃癌患者的生存可能性可为护理人员提供信息，以改善患者预后并选择最佳治疗路径。本研究旨在开发一种基于机器学习（ML）算法的智能系统，用于预测胃癌患者的5年生存状况。
回顾性使用了一个包含974例胃癌患者记录的数据集。首先，使用Boruta特征选择算法识别最重要的预测因子。训练了五个分类器，包括J48决策树（DT）、具有径向基函数（RBF）核的支持向量机（SVM）、装袋法（Bagging）、直方图梯度提升（HGB）和自适应提升（AdaBoost），以预测胃癌生存情况。使用特异性、敏感性、似然比和总准确率评估所使用技术的性能。最后，根据最佳模型开发了该系统。
肿瘤分期、位置和大小被选为胃癌生存的3个最重要预测因子。在6种选定的ML算法中，HGB分类器的平均准确率、平均特异性、平均敏感性、平均曲线下面积和平均F1分数分别为88.37%、86.24%、89.72%、88.11%和89.91%，表现最佳。
ML模型可以准确预测5年生存率，并有可能作为胃癌患者决策的定制推荐工具。我们研究中开发的系统可以提高治疗质量、患者安全性和生存率；它可能有助于开出更个性化的药物处方。
