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A Cost-Effective Model for Predicting Recurrent Gastric Cancer Using Clinical Features

Authors :
Chun-Chia Chen
Wen-Chien Ting
Hsi-Chieh Lee
Chi-Chang Chang
Tsung-Chieh Lin
Shun-Fa Yang
Source :
Diagnostics, Vol 14, Iss 8, p 842 (2024)
Publication Year :
2024
Publisher :
MDPI AG, 2024.

Abstract

This study used artificial intelligence techniques to identify clinical cancer biomarkers for recurrent gastric cancer survivors. From a hospital-based cancer registry database in Taiwan, the datasets of the incidence of recurrence and clinical risk features were included in 2476 gastric cancer survivors. We benchmarked Random Forest using MLP, C4.5, AdaBoost, and Bagging algorithms on metrics and leveraged the synthetic minority oversampling technique (SMOTE) for imbalanced dataset issues, cost-sensitive learning for risk assessment, and SHapley Additive exPlanations (SHAPs) for feature importance analysis in this study. Our proposed Random Forest outperformed the other models with an accuracy of 87.9%, a recall rate of 90.5%, an accuracy rate of 86%, and an F1 of 88.2% on the recurrent category by a 10-fold cross-validation in a balanced dataset. We identified clinical features of recurrent gastric cancer, which are the top five features, stage, number of regional lymph node involvement, Helicobacter pylori, BMI (body mass index), and gender; these features significantly affect the prediction model’s output and are worth paying attention to in the following causal effect analysis. Using an artificial intelligence model, the risk factors for recurrent gastric cancer could be identified and cost-effectively ranked according to their feature importance. In addition, they should be crucial clinical features to provide physicians with the knowledge to screen high-risk patients in gastric cancer survivors as well.

Details

Language :
English
ISSN :
14080842 and 20754418
Volume :
14
Issue :
8
Database :
Directory of Open Access Journals
Journal :
Diagnostics
Publication Type :
Academic Journal
Accession number :
edsdoj.5a33e67494da4ce1973330c3810208d2
Document Type :
article
Full Text :
https://doi.org/10.3390/diagnostics14080842