S&M2645 Research Paper of Special Issue
Published: August 10, 2021
Classification of Esophageal Adenocarcinoma, Esophageal Squamous Cell Carcinoma, and Stomach Adenocarcinoma Based on Machine Learning Algorithms [PDF]
Xiaoping Chen, Lihui Zheng, Jianqi Yao, and Cheng-Fu Yang
(Received March 23, 2021; Accepted June 17, 2021)
Keywords: EAC, ESCC, SAC, machine learning algorithm, confusion matrix
Esophageal and gastric cancers are common malignant tumors. In medicine, it is difficult to differentiate the sickness symptoms of esophageal adenocarcinoma (EAC), esophageal squamous cell carcinoma (ESCC), and stomach adenocarcinoma (SAC). In particular, the molecular characteristics of EAC and SAC are very similar, which makes them difficult to distinguish. Information collected by sensors can be analyzed by machine learning. In this study, we used cancer data published in Nature in 2017, which were downloaded from cBioPortal, to classify the three types of cancer by five machine learning algorithms, and we compared the classification effects for different models by calculating confusion matrices. According to the research data in this paper, the random forest (RF) model is the best of the five machine learning classification models for the overall classification effect of the three types of cancer. More specifically, the classification effect of this model is the best for EAC, whereas the classification effect for ESCC is not ideal. The classification based on the RF model can effectively enhance the differentiation between the symptoms of EAC, SAC, and ESCC, enabling cancer patients to receive more accurate treatment and have an improved prognosis.
Corresponding author: Jianqi Yao, Cheng-Fu Yang
This work is licensed under a Creative Commons Attribution 4.0 International License.
Cite this article
Xiaoping Chen, Lihui Zheng, Jianqi Yao, and Cheng-Fu Yang, Classification of Esophageal Adenocarcinoma, Esophageal Squamous Cell Carcinoma, and Stomach Adenocarcinoma Based on Machine Learning Algorithms, Sens. Mater., Vol. 33, No. 8, 2021, p. 2639-2654.