S&M Young Researcher Paper Award 2020
Recipients: Ding Jiao, Zao Ni, Jiachou Wang, and Xinxin Li [Winner's comments]
Paper: High Fill Factor Array of Piezoelectric Micromachined
Ultrasonic Transducers with Large Quality Factor

S&M Young Researcher Paper Award 2021
Award Criteria
Notice of retraction
Vol. 32, No. 8(2), S&M2292

Print: ISSN 0914-4935
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Sensors and Materials
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.

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S&M2645 Research Paper of Special Issue

Classification of Esophageal Adenocarcinoma, Esophageal Squamous Cell Carcinoma, and Stomach Adenocarcinoma Based on Machine Learning Algorithms

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 that was downloaded from cBioPortal to classify the three types of cancers 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 model is the best of the five machine learning classification models for the overall classification effect of the three types of cancers. 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 random forest 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




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