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

ACS Sensors, Год журнала: 2025, Номер unknown

Опубликована: Апрель 17, 2025

The performance of electrochemical sensors is influenced by various factors. To enhance the effectiveness these sensors, it crucial to find right balance among Researchers and engineers continually explore innovative approaches sensitivity, selectivity, reliability. Machine learning (ML) techniques facilitate analysis predictive modeling sensor establishing quantitative relationships between parameters their effects. This work presents a case study on developing molecularly imprinted polymer (MIP)-based for detecting doxorubicin (Dox), emphasizing use ML-based ensemble models improve Four ML models, including Decision Tree (DT), eXtreme Gradient Boosting (XGBoost), Random Forest (RF), K-Nearest Neighbors (KNN), are used evaluate effect each parameter prediction performance, using SHapley Additive exPlanations (SHAP) method determine feature importance. Based analysis, removing less influential introducing new significantly improved model's capabilities. By applying min-max scaling technique, ensured that all features contribute proportionally model process. Additionally, multiple models─Linear Regression (LR), KNN, DT, RF, Adaptive (AdaBoost), (GB), Support Vector (SVR), XGBoost, Bagging, Partial Least Squares (PLS), Ridge Regression─are applied data set in predicting output current compared. further novel proposed integrates GB, Bagging regressors, leveraging combined strengths offset individual weaknesses. main benefit this lies its ability MIP-based stacking regressor model, which improves methodology broadly applicable development other with different transducers sensing elements. Through extensive simulation results, demonstrated superior compared models. achieved an R-squared (R2) 0.993, reducing root-mean-square error (RMSE) 0.436 mean absolute (MAE) 0.244. These improvements enhanced sensitivity reliability sensor, demonstrating substantial gain over

Язык: Английский

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AI-Based solutions for current challenges in regenerative medicine

European Journal of Pharmacology, Год журнала: 2024, Номер unknown, С. 177067 - 177067

Опубликована: Окт. 1, 2024

Язык: Английский

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An integrated framework for rainfall prediction and analysis using a Stacked Heterogeneous Ensemble Model (SHEM)

Expert Systems with Applications, Год журнала: 2024, Номер 256, С. 124831 - 124831

Опубликована: Авг. 6, 2024

Язык: Английский

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The Lag -Effects of Air Pollutants and Meteorological Factors on COVID-19 Infection Transmission and Severity: Using Machine Learning Techniques

Journal of Research in Health Sciences, Год журнала: 2024, Номер 24(3), С. e00622 - e00622

Опубликована: Июль 31, 2024

Exposure to air pollution is a major health problem worldwide. This study aimed investigate the effect of level pollutants and meteorological parameters with their related lag time on transmission severity coronavirus disease 19 (COVID-19) using machine learning (ML) techniques in Shiraz, Iran.

Язык: Английский

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Thyroid disease diagnosis based on feature interpolation Interaction and dynamic assignment Stacking model

Biomedical Signal Processing and Control, Год журнала: 2024, Номер 101, С. 107207 - 107207

Опубликована: Ноя. 25, 2024

Язык: Английский

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