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摘要
Introduction. Early cancer detection improves survival rates and reduces healthcare costs due to less treatment intensification, fewer hospitalizations and higher chances of remission. There is an increasing need for practical, interpretable screening tools that can effectively assess cancer risk and enable early identification of high-risk patients for timely intervention.
Aim. To develop and externally validate machine learning models for predicting the probability of cancer development in patients based on real-world clinical data.
Materials and Methods. Depersonalized HER data from 1.3 million patients were used for the study. Gender, age, weight change rate, erythrocyte sedimentation rate, hemoglobin, body mass index, and history of clinically significant comorbidities were considered as potential predictors. These comorbidities were defined by a medical expert and encompassed 54 conditions with documented associations with cancer risk. The performances of Logistic Regression, LGBMClassifier, Random Forest, Linear Discriminant Analysis, and Naive Bayes classifier models were compared. External validation was performed using data with different regional origin
Results. The LGBMClassifier demonstrated the best discrimination, with an AUROC of 0.807 (95% CI 0.798–0.815) during testing, 0.794 (95% CI 0.786–0.800) on external data set №1, and 0.790 (95% CI 0.782–0.798) on external data set №2. The model showed good calibration and utility metrics. Meta-evaluation results confirmed the its robustness.
Conclusion. The performance metrics of the model were superior to those in previously published studies. External validation results demonstrated the model's relative stability when applied to novel, independent data, confirming its potential for the clinical practice.
参考
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