Prognostic Survival Prediction of Patients with Liver Cirrhosis based on Radiomics Data and Clinical Features
Authors: 
Lun Lin, Yinghui Jin, Wenxin HuAuthors Info & Claims
Lun Lin, Yinghui Jin, Wenxin HuAuthors Info & ClaimsPages 141 - 147
Abstract
Assessing a cirrhosis patient's prognostic life expectancy and providing accurate management is critical to reducing the risk of death. However, survival prediction tasks are often affected by the issue of sample imbalance, which will impact the accuracy of survival predictions. Therefore, based on radiomics and clinical data, we propose a novel MLP-Ensemble model for the 2-year survival prediction of patients with liver cirrhosis. It utilizes neural networks as the base model, integrating undersampling techniques and ensemble learning methods to efficiently handle high-dimensional features, significantly enhancing the model's ability to predict minority class samples. After conducting a five-fold cross-validation on the radiomics-clinic dataset of 112 patients, our model has achieved an Accuracy of 95.6%, significantly outperforming other classic models. At the same time, our model also exhibits a higher sensitivity. With the validation of several experiments, it has been proved that this MLP-Ensemble model can improve the ability to deal with imbalanced complex data.
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Index Terms
- Prognostic Survival Prediction of Patients with Liver Cirrhosis based on Radiomics Data and Clinical Features
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Published In
May 2024
668 pages
ISBN:9798400716751
DOI:10.1145/3670105
Copyright © 2024 ACM.
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Association for Computing Machinery
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Published: 29 July 2024
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CNIOT 2024
CNIOT 2024: 2024 5th International Conference on Computing, Networks and Internet of Things
May 24 - 26, 2024
Tokyo, Japan
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Overall Acceptance Rate 39 of 82 submissions, 48%
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