Enhancing the Predictive Performance of Molecularly Imprinted Polymer-Based Electrochemical Sensors Using a Stacking Regressor Ensemble of Machine Learning Models

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.