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 Ridge Regression─are applied to the data set and their performance in predicting the sensor output current is compared. To further enhance prediction performance, a novel ensemble model is proposed that integrates DT, RF, GB, XGBoost, and Bagging regressors, leveraging their combined strengths to offset individual weaknesses. The main benefit of this work lies in its ability to enhance MIP-based sensor performance by developing a novel stacking regressor ensemble model, which improves prediction performance and reliability. This methodology is broadly applicable to the development of other sensors with different transducers and sensing elements. Through extensive simulation results, the proposed stacking regressor ensemble model demonstrated superior predictive performance compared to individual ML models. The model achieved an R-squared (R²) of 0.993, significantly reducing the root-mean-square error (RMSE) to 0.436 and the mean absolute error (MAE) to 0.244. These improvements enhanced sensitivity and reliability of the MIP-based electrochemical sensor, demonstrating a substantial performance gain over individual ML models.

Keywords: electrochemical sensors; feature engineering; machine learning; molecularly imprinted polymers; stack-based ensemble regressor.