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Serum levels of chemical elements in esophageal squamous cell carcinoma in Anyang, China: a case-control study based on machine learning methods

Authors :
Yucheng Lin
Lailai Yan
Tong Lin
Chaoting Zhang
Tiebing Liu
Zhongxue Chen
Zhonghu He
Jingyu Wang
Source :
BMJ Open
Publication Year :
2017
Publisher :
BMJ Publishing Group, 2017.

Abstract

ObjectivesEsophageal squamous cell carcinoma (ESCC) is the predominant form of esophageal carcinoma with extremely aggressive nature and low survival rate. The risk factors for ESCC in the high-incidence areas of China remain unclear. We used machine learning methods to investigate whether there was an association between the alterations of serum levels of certain chemical elements and ESCC.SettingsPrimary healthcare unit inAnyangcity, Henan Province of China.Participants100 patients with ESCC and 100 healthy controls matched for age, sex and region were included.Primary and secondary outcome measuresPrimary outcome was the classification accuracy. Secondary outcome was the p Value of the t-test or rank-sum test.MethodsBoth traditional statistical methods of t-test and rank-sum test and fashionable machine learning approaches were employed.ResultsRandom Forest achieves the best accuracy of 98.38% on the original feature vectors (without dimensionality reduction), and support vector machine outperforms other classifiers by yielding accuracy of 96.56% on embedding spaces (with dimensionality reduction). All six classifiers can achieve accuracies more than 90% based on the single most important element Sr. The other two elements with distinctive difference are S and P, providing accuracies around 80%. More than half of chemical elements were found to be significantly different between patients with ESCC and the controls.ConclusionsThese results suggest clear differences between patients with ESCC and controls, implying some potential promising applications in diagnosis, prognosis, pharmacy and nutrition of ESCC. However, the results should be interpreted with caution due to the retrospective design nature, limited sample size and the lack of several potential confounding factors (including obesity, nutritional status, and fruit and vegetable consumption and potential regional carcinogen contacts).

Details

Language :
English
ISSN :
20446055
Volume :
7
Issue :
9
Database :
OpenAIRE
Journal :
BMJ Open
Accession number :
edsair.doi.dedup.....507a788e4c45de74aee9ef0d93d3c0c8