HIGHLIGHTS
SUMMARY
The study aimed to develop SA prediction models based on sociodemographic, clinical, and lifestyle factors, which can be used for early prediction of SA and to explore important predictors affecting its further progress. To predict whether a person has SA or non-SA status, five basic classification algorithms, including artificial neural_network (ANN), decision tree (DT), support vector machine (SVM), Naïve Bayes (NB), and k-nearest neighbors (K-NN) models were first trained. To promote the prediction accuracy of the models, a hybrid model called ensemble-based KNN was developed. Lin et_al also compared . . .
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