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Early screening and risk assessment of nasopharyngeal carcinoma
(NPC) are essential for timely diagnosis and improved treatment outcomes. This
study aimed to develop and evaluate predictive models using logistic regression
and machine learning (ML) techniques to identify significant risk factors for NPC
across various healthcare settings.
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VCA:
Rta:
NTA1:
L/M:
PLT:
Principle:
First divide the data into training set and test set, then use the cross-validation method to train the model in the training set, train the optimal model as the final model and record the threshold at this time as the final threshold, and finally observe the model in the test performance on the set. By continuously adjusting the parameters of the model, the generalization ability of the model is improved, and the performance of the model in the training set, validation set and test set is relatively optimal.
By the predicted sample into the optimal model, the model will predict the probability of occurrence, generate the SHAP force plot, and then evaluate the risk according to the predicted probability and prediction.