Enhancing the Predictive Performance of Molecularly Imprinted Polymer-Based Electrochemical Sensors Using a Stacking Regressor Ensemble of Machine Learning Models
Abstract
The performance of electrochemical sensors is influenced by various factors. To enhance the effectiveness of these sensors, it is crucial to find the right balance among these factors. Researchers and engineers continually explore innovative approaches to enhance sensitivity, selectivity, and reliability. Machine learning (ML) techniques facilitate the analysis and predictive modeling of sensor performance by establishing quantitative relationships between parameters and their effects. This work presents a case study on developing a molecularly imprinted polymer (MIP)-based sensor for detecting doxorubicin (Dox), emphasizing the use of ML-based ensemble models to improve performance and reliability. Four ML models, including Decision Tree (DT), eXtreme Gradient Boosting (XGBoost), Random Forest (RF), and K-Nearest Neighbors (KNN), are used to evaluate the effect of each parameter on prediction performance, using the SHapley Additive exPlanations (SHAP) method to determine feature importance. Based on the analysis, removing a less influential feature and introducing a new feature significantly improved the model's predictive capabilities. By applying the min-max scaling technique, it is ensured that all features contribute proportionally to the model learning process. Additionally, multiple ML models─Linear Regression (LR), KNN, DT, RF, Adaptive Boosting (AdaBoost), Gradient Boosting (GB), Support Vector Regression (SVR), XGBoost, Bagging, Partial Least Squares (PLS), and…
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