Application of the Random Forest-MLP Algorithm in the Recognition of Esophageal Cancer

Zhixiang Xu; Wenpei Zhang; Mingming Li; Weihao Li; Weihong Chen

doi:10.47297/taposatWSP2633-456925.20250606

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Application of the Random Forest-MLP Algorithm in the Recognition of Esophageal Cancer

Volume 6, Issue 6

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Application of the Random Forest-MLP Algorithm in the Recognition of Esophageal Cancer
Application of the Random Forest-MLP Algorithm in the Recognition of Esophageal Cancer
School of Mathematics and Statistics, Kashi University, Kashi, Xinjiang, 844000, China
Vol. 6, Issue 6, Pages: 125-130(2025)
Published：30 June 2025
DOI：10.47297/taposatWSP2633-456925.20250606
Accepted：

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Xu Zhixiang,Zhang Wenpei,Li Mingming,et al.Application of the Random Forest-MLP Algorithm in the Recognition of Esophageal Cancer[J].Theory and Practice of Science and Technology,2025,06(06):125-130.
DOI：

Xu Zhixiang,Zhang Wenpei,Li Mingming,et al.Application of the Random Forest-MLP Algorithm in the Recognition of Esophageal Cancer[J].Theory and Practice of Science and Technology,2025,06(06):125-130. DOI： 10.47297/taposatWSP2633-456925.20250606.

摘要

Abstract

This research focuses on the application of the Random Forest (RF) and Multilayer Perceptron (MLP) combined model in the field of esophageal cancer recognition. The incidence of esophageal cancer is relatively high in China

and accurate prediction is of great significance for improving the survival rate of patients. With the help of 85 types of esophageal cancer data provided by the Kaggle platform

which cover rich information such as clinical features

pathological diagnosis results

treatment methods and prognostic data

this study first uses random forest to carry out feature screening and accurately identifies the key features that have a significant impact on esophageal cancer prediction. Then

these selected features are input into the MLP model for training and verification. The experimental results show that the RF-MLP model significantly outperforms the traditional decision tree

support vector machine and adaptive boosting algorithm in terms of accuracy (as high as 98.746%) and precision (reaching 100%)

fully demonstrating its great potential in the early diagnosis of esophageal cancer

building a solid and effective technical support for the risk prediction of esophageal cancer

and effectively promoting the development process of related research and clinical application.

关键词

Keywords

references

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