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A machine learning model for predicting the three-year survival status of patients with hypopharyngeal squamous cell carcinoma using multiple parameters
Published online by Cambridge University Press: 23 January 2023
Abstract
Objective
This study aimed to establish a model for predicting the three-year survival status of patients with hypopharyngeal squamous cell carcinoma using artificial intelligence algorithms.
Method
Data from 295 patients with hypopharyngeal squamous cell carcinoma were analysed retrospectively. Training sets comprised 70 per cent of the data and test sets the remaining 30 per cent. A total of 22 clinical parameters were included as training features. In total, 12 different types of machine learning algorithms were used for model construction. Accuracy, sensitivity, specificity, area under the receiver operating characteristic curve and Cohen's kappa co-efficient were used to evaluate model performance.
Results
The XGBoost algorithm achieved the best model performance. Accuracy, sensitivity, specificity, area under the receiver operating characteristic curve and kappa value of the model were 80.9 per cent, 92.6 per cent, 62.9 per cent, 77.7 per cent and 58.1 per cent, respectively.
Conclusion
This study successfully identified a machine learning model for predicting three-year survival status for patients with hypopharyngeal squamous cell carcinoma that can offer a new prognostic evaluation method for the clinical treatment of these patients.
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- Copyright
- Copyright © The Author(s), 2023. Published by Cambridge University Press on behalf of J.L.O. (1984) LIMITED
Footnotes
Dr Y Zhang takes responsibility for the integrity of the content of the paper
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