Establishment and Analysis of a Combined Diagnostic Model of Liver Cancer with Random Forest and Artificial Neural Network

Establishment and Analysis of a Combined Diagnostic Model of Liver Cancer with Random Forest and Artificial Neural Network

The incidence of liver cancer (hepatocellular carcinoma; HCC) is rising and with poor clinical outcome expected, a more accurate judgment of tumor tissues and adjacent nontumor tissues is necessary. The aim of this study was to construct a diagnostic model based on random forest (RF) and artificial...

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Veröffentlicht in:Mathematical problems in engineering 2022-05, Vol.2022, p.1-17
Hauptverfasser: Yu, Runzhi, Cao, Ziyi, Huang, Yiqin, Zhang, Xuechun, Chen, Jie
Format: Artikel
Sprache:eng
Schlagworte:
Accuracy
Artificial intelligence
Artificial neural networks
Cancer therapies
Correlation analysis
Data collection
Datasets
Diagnosis
Gene expression
Genes
Immune system
Liver cancer
Liver diseases
Neural networks
Software
Training
Tumors
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Zusammenfassung:The incidence of liver cancer (hepatocellular carcinoma; HCC) is rising and with poor clinical outcome expected, a more accurate judgment of tumor tissues and adjacent nontumor tissues is necessary. The aim of this study was to construct a diagnostic model based on random forest (RF) and artificial neural network (ANN). It can be used to aid in the identification of diseased tissue such as cancerous tissue, for HCC clinical diagnosis and surgical guidance. GSE36376 and GSE121248 from Gene Expression Omnibus (GEO) were used as training sets in this investigation. R package “limma” and WGCNA were used to filter the training set for statistically significant p
ISSN:1024-123X
1563-5147
DOI:10.1155/2022/5679837