Abstract: With the advancement of sensor technology,blood glucose monitoring has evolved from traditional single-point sampling to continuous dynamic monitoring CGM,enabling real-time monitoring of interstitial fluid glucose concentration through interventional glucose sensor.The operational status of glucose sensors is crucial for monitoring accuracy,but sensor fault detection faces the challenge of class imbalance,leading to degraded performance of machine learning models.Based on this,this paper proposes an optimization strategy that combines data preprocessing,feature engineering,and model integration.Firstly,the completeness and reliability of the data are improved through missing value imputation and noise reduction.Secondly,the SMOTE is used to oversample minority class samples,alleviating the class imbalance problem.Finally,a two-layer model is constructed using the Stacking ensemble learning method,which combines base classifiers of XGBoost optimized with Focal Loss and CatBoost with a LR meta-classifier,further enhancing the accuracy of fault monitoring.To demonstrate the effectiveness of the proposed model,its prediction results are compared with other models,including a single XGBoost model based on Focal Loss and ensemble models constructed with SVM,KNN,and LightGBM as base classifiers.The results show that the proposed XGBoost optimized with the Focal Loss and CatBoost models,perform well in the sensor fault classification task,with both PR and ROC curves outperforming other models,achieving precision and recall rates of 0.925 0 and 0.923 8,respectively.

Key words: Sensor fault detection, Stacked generalization, Ensemble learning, Extreme Gradient Boosting, Categorical Boosting