Machine learning‐based analysis of interaction effects among influencing factors on the resilient modulus of stabilized aggregate base

Several machine learning classifiers were trained to predict transplantation of a liver graft. We utilized 127 variables available in the DMG dataset. We included data from potential deceased organ donors between April 2012 and January 2019. The outcome was defined as liver recovery for transplantation in the operating room. The prediction was made based on data available 12-18 h after the time of authorization for transplantation. The data were randomly separated into training (60%), validation (20%), and test sets (20%). We compared the performance of our models to the Liver Discard Risk Index. Of 13 629 donors in the dataset, 9255 (68%) livers were recovered and transplanted, 1519 recovered but used for research or discarded, 2855 were not recovered. The optimized gradient boosting machine classifier achieved an area under the curve of the receiver operator characteristic of 0.84 on the test set, outperforming all other classifiers. This model predicts successful liver recovery for transplantation in the operating room, using data available early during donor management. It performs favorably when compared to existing models. It may provide real-time decision support during organ donor management and transplant logistics.

The incidence of early-onset gastroenteropancreatic neuroendocrine tumors (GEP-NETs) is increasing, with liver metastases often occurring early and adversely affecting prognosis. This study aimed to develop a predictive model for liver metastases detection in patients with early-onset GEP-NETs (<50 years) using an automated machine learning (AutoML) approach. A retrospective analysis was conducted on patients diagnosed with early-onset GEP-NETs [2000-2021] using data from the Surveillance, Epidemiology, and End Results (SEER) database. Patients were randomly divided into a training set (n=8,983) and a validation set (n=3,819) in a 7:3 ratio. A nomogram-based scoring system was constructed using least absolute shrinkage and selection operator (LASSO) and logistic regression. AutoML was applied to build predictive models using gradient boosting machine (GBM), generalized linear model (GLM), deep learning (DL), and distributed random forest (DRF) algorithms. Model performance was assessed using receiver operating characteristic (ROC), calibration, decision curve analysis (DCA), and interpretability tools including SHapley Additive exPlanations (SHAP), partial dependence plots (PDPs), and locally interpretable model-agnostic explanations (LIME) plots. A total of 12,802 patients were included, of whom 1,187 (9.3%) developed liver metastases, comprising 851 (9.5%) and 336 (8.8%) cases in the training and validation sets, respectively. Comparative analyses demonstrated that the AutoML models outperformed traditional logistic regression models, with the GBM algorithm achieving the highest performance. The GBM model achieved an area under the curve (AUC) of 0.961 in the training set and 0.953 in the validation set. Tumor location was identified as the most important predictor in the GBM model, followed by surgery, tumor size, chemotherapy, and T-staging. The AutoML model leveraging the GBM algorithm provides a robust and clinically valuable tool for the early prediction of liver metastases in patients with early-onset GEP-NETs.

Tree-based machine learning (ML) algorithms, such as Extra Trees (ET), Random Forest (RF), Gradient Boosting Machine (GBM), and XGBoost (XGB) are among the most widely used in early drug discovery, given their versatility and performance. However, models based on these algorithms often suffer from misclassification and reduced interpretability issues, which limit their applicability in practice. To address these challenges, several approaches have been proposed, including the use of SHapley Additive Explanations (SHAP). While SHAP values are commonly used to elucidate the importance of features driving models' predictions, they can also be employed in strategies to improve their prediction performance. Building on these premises, we propose a novel approach that integrates SHAP and features value analyses to reduce misclassification in model predictions. Specifically, we benchmarked classifiers based on ET, RF, GBM, and XGB algorithms using data sets of compounds with known antiproliferative activity against three prostate cancer (PC) cell lines (i.e., PC3, LNCaP, and DU-145). The best-performing models, based on RDKit and ECFP4 descriptors with GBM and XGB algorithms, achieved MCC values above 0.58 and F1-score above 0.8 across all data sets, demonstrating satisfactory accuracy and precision. Analyses of SHAP values revealed that many misclassified compounds possess feature values that fall within the range typically associated with the opposite class. Based on these findings, we developed a misclassification-detection framework using four filtering rules, which we termed "RAW", SHAP, "RAW OR SHAP", and "RAW AND SHAP". These filtering rules successfully identified several potentially misclassified predictions, with the "RAW OR SHAP" rule retrieving up to 21%, 23%, and 63% of misclassified compounds in the PC3, DU-145, and LNCaP test sets, respectively. The developed flagging rules enable the systematic exclusion of likely misclassified compounds, even across progressively higher prediction confidence levels, thus providing a valuable approach to improve classifier performance in virtual screening applications.

BackgroundNeonatal Intrahepatic Cholestasis caused by Citrin Deficiency (NICCD) is an autosomal recessive disorder affecting the urea cycle and energy metabolism. Newborn screening (NBS) usually relies on elevated citrulline, but some patients have normal citrulline, resulting in false negatives and delayed diagnosis. This study develops an explainable machine learning (ML) model to predict false-negative NICCD cases during NBS.MethodsData from 53 false-negative NICCD patients and 212 controls, collected retrospectively between 2011 and 2024, were analyzed. The dataset was split into a training set (70%) and a test set (30%). External validation involved 48 participants from distinct time periods. Key predictors were identified using variable importance in projection (VIP > 1) and Lasso regression. Six ML models were trained for evaluation: Logistic Regression, Random Forest, Light Gradient Boosting Machine, Extreme Gradient Boosting (XGBoost), K-Nearest Neighbor, and Support Vector Machines. Performance was evaluated using the area under the receiver operating characteristic curve (AUC) and F1 score. Shapley Additive exPlanations (SHAP) was applied to determine the importance of features and interpret the models.ResultsBirth weight, citrulline, glycine, phenylalanine, ornithine, arginine, proline, succinylacetone, and C10:2 were selected as predictive features. Among the ML models, XGBoost demonstrated the most robust and consistent performance, achieving AUCs of 0.971(95%CI: 0.959–0.979), 0.968, and 0.977, and F1 scores of 0.786(95% CI: 0.744–0.820), 0.828, and 0.833 in the training, test, and external validation sets, respectively. SHAP analysis showed that the most important features are citrulline, glycine, phenylalanine, succinylacetone, birth weight, and ornithine. Feature pairs such as citrulline-phenylalanine, citrulline-glycine, succinylacetone-birth weight, and ornithine-glycine showed varying interactions. SHAP force plots, decision plots, and waterfall plots provided insightful patient-level interpretations. Finally, we built a network calculator for the prediction of false-negative NICCD cases (https://myapp123.shinyapps.io/my_shiny_app/).ConclusionAn interpretable machine learning model utilizing metabolite and demographic data enhances the detection of false-negative NICCD cases, facilitates early identification and intervention, and ultimately improves the overall effectiveness of the newborn screening system.Supplementary InformationThe online version contains supplementary material available at 10.1186/s13023-025-04045-z.

Background The use of incinerated bottom ash (IBA) as a sustainable construction material offers potential environmental benefits but introduces complex interactions with cement chemistry. Magnesium phosphate cement (MPC), known for its rapid hardening and superior bonding, can be optimized through the controlled incorporation of IBA. However, limited studies have addressed how the chemical components of IBA affect the compressive strength of MPC, particularly using data-driven approaches. Methods A database of 396 experimental samples was compiled from previous studies considering mix proportions, oxide compositions, and curing conditions. Four ensemble machine learning algorithms—Extreme Gradient Boosting (XGB), Light Gradient Boosting (LGB), Gradient Boosting Regressor (GBR), and Random Forest (RFR)—were employed to predict compressive strength. Model robustness was validated through 5-fold cross-validation. Feature interpretation was achieved using SHapley Additive exPlanations (SHAP) and Partial Dependence Plots (PDP) to quantify individual and interactive effects of chemical and physical parameters. Results The XGB model achieved the highest predictive accuracy, with mean training and testing R2 values greater than 0.90 and 0.80, and the lowest mean absolute percentage error of 16.71%. SHAP analysis identified curing age as the most dominant factor, followed by FA/C, W/C, and MgO/PO4 ratios. IBA content and specific oxides such as Fe2O3 and Al2O3 contributed positively to strength within optimal ranges. PDP confirmed nonlinear dependencies, indicating a 26% reduction in strength as W/C increased from 0.1 to 0.6, while extended curing up to 28 days improved performance substantially. Conclusion The integration of SHAP and PDP provided a transparent interpretation of feature interactions in IBA-modified MPC. The developed XGB model demonstrated strong generalization and interpretability. The combined modeling approach offers a reliable predictive framework for optimizing IBA incorporation in sustainable binder systems and advancing eco-efficient material design.

Prediction of gully erosion susceptibility through the lens of the SHapley Additive exPlanations (SHAP) method using a stacking ensemble model.

Multi-output machine learning for predicting the mechanical properties of BFRC

Hybrid gradient boosting with meta-heuristic algorithms prediction of unconfined compressive strength of stabilized soil based on initial soil properties, mix design and effective compaction

Machine learning approach for investigating chloride diffusion coefficient of concrete containing supplementary cementitious materials

&amp;lt;i&amp;gt;Objectives:&amp;lt;/i&amp;gt; The aim of this study was to construct depression prediction models based on machine learning algorithms, compared the performance of different machine learning models on depression risk prediction, and interpreted the model. &amp;lt;i&amp;gt;Methods:&amp;lt;/i&amp;gt; A total of 2573 participants from the CHARLS database. LASSO and stepwise regression were used to screen for variables. The dataset is randomly divided into training set, validation set and test set according to 6:2:2. SMOTE resampling was used to balance the training set when fitted the model. Nine machine learning algorithms were used to construct the prediction model, inclpuding Decision Tree (DT), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Elastic Network Regression (Enet), Support Vector Machine (SVM), Logistic Regression, Multilayer Perceptron (MLP), and K-Nearest Neighbor (KNN). The prediction ability of each machine learning classifier was evaluated on the test set according to the evaluation index, and the &amp;quot;optimal&amp;quot; model of this study was selected. Subsequently, SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) were used to analyze the interpretability of the optimal model. &amp;lt;i&amp;gt;Results&amp;lt;/i&amp;gt;: The XGBoost model predicted the best performance among the 9 models. Its AUC value reached 0.908 and the clinical net benefit is the highest. The Delong test showed that there was a significant difference between the ROC curves of XGBoost and the other models (&amp;lt;I&amp;gt;P&amp;lt;/I&amp;gt;&amp;lt;0.05). The global interpretation based on SHAP showed that life satisfaction, self-rated health status, sleep duration, and cognitive score were inversely proportional to the SHAP value. Female, rural residents, body aches and pains in any area, non-retirement, and limited Instrumental Activities of Daily Living (IADL) have a positive effect on depression. The local interpretation diagram based on SHAP and LIME showed the personalized risk prediction of a single sample. &amp;lt;i&amp;gt;Conclusions:&amp;lt;/i&amp;gt; Machine learning models are an effectively tool for predict the risk of depression. The use of SHapley Additive exPlanations and Local Interpretable Model-agnostic Explanations can maximize the clinical advantages of machine learning, which is helpful to predict or detect patients at high risk of depression as early as possible, and to take comprehensive evaluation and early prevention and treatment of depression.

Optimisation and interpretation of machine and deep learning models for improved water quality management in Lake Loktak

Identifying cardiovascular disease risk in the U.S. population using environmental volatile organic compounds exposure: A machine learning predictive model based on the SHAP methodology

BackgroundWeaning from mechanical ventilation (MV) is an essential issue in critically ill patients, and we used an explainable machine learning (ML) approach to establish an extubation prediction model.MethodsWe enrolled patients who were admitted to intensive care units during 2015–2019 at Taichung Veterans General Hospital, a referral hospital in central Taiwan. We used five ML models, including extreme gradient boosting (XGBoost), categorical boosting (CatBoost), light gradient boosting machine (LightGBM), random forest (RF) and logistic regression (LR), to establish the extubation prediction model, and the feature window as well as prediction window was 48 h and 24 h, respectively. We further employed feature importance, Shapley additive explanations (SHAP) plot, partial dependence plot (PDP) and local interpretable model-agnostic explanations (LIME) for interpretation of the model at the domain, feature, and individual levels.ResultsWe enrolled 5,940 patients and found the accuracy was comparable among XGBoost, LightGBM, CatBoost and RF, with the area under the receiver operating characteristic curve using XGBoost to predict extubation was 0.921. The calibration and decision curve analysis showed well applicability of models. We also used the SHAP summary plot and PDP plot to demonstrate discriminative points of six key features in predicting extubation. Moreover, we employed LIME and SHAP force plots to show predicted probabilities of extubation and the rationale of the prediction at the individual level.ConclusionsWe developed an extubation prediction model with high accuracy and visualised explanations aligned with clinical workflow, and the model may serve as an autonomous screen tool for timely weaning.

BackgroundPatients with gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs) and liver metastases typically exhibit poor prognoses. However, accurate survival prediction models remain insufficient. This study aimed to develop machine learning-based models to predict the 1-year, 3-year, and 5-year overall survival in these patients.MethodsWe retrospectively analyzed patients diagnosed with GEP-NENs and liver metastases from the Surveillance, Epidemiology, and End Results (SEER) database. Patients were randomly divided into training and testing sets in a 7:3 ratio. Seven machine learning models were constructed: cox regression, lasso regression, random survival forest (RSF), extreme gradient boosting (XGBoost), decision tree, gradient boosting machine (GBM), and neural network. Model performance was evaluated using C-index, AUC, Calibration curve, Brier score, and decision curve analysis (DCA). The optimal model was further interpreted through variable importance analysis, partial dependence plots, and individual prediction plots.ResultsA total of 4,528 patients were included, with 3,165 in the training set and 1,363 in the testing set. Among the seven models, the RSF model demonstrated the best overall performance. In the training set, it achieved a C-index of 0.815, with 1-year, 3-year, and 5-year AUC values of 0.895, 0.907, and 0.905, respectively, and Brier scores of 0.121, 0.128, and 0.128. The calibration curve shows good predictive performance, while the DCA highlights its strong net benefit in clinical decision-making. In the testing set, it maintained robust performance (C-index: 0.785; AUC: 0.855/0.859/0.841). The five most influential variables in the RSF model were tumor grade, surgical intervention, tumor site, age, and histology.ConclusionThe RSF model provides a reliable tool for predicting overall survival in GEP-NEN patients with liver metastases.

Increasing and compelling evidence has been proved that heavy metal exposure is involved in the development of insulin resistance (IR). We trained an interpretable predictive machine learning (ML) model for IR in the non-diabetic populations based on levels of heavy metal exposure. A total of 4354 participants from the NHANES (2003-2020) with complete information were randomly divided into a training set and a test set. Twelve ML algorithms, including random forest (RF), XGBoost (XGB), logistic regression (LR), GaussianNB (GNB), ridge regression (RR), support vector machine (SVM), multilayer perceptron (MLP), decision tree (DT), AdaBoost (AB), Gradient Boosting Decision Tree (GBDT), Voting Classifier (VC), and K-Nearest Neighbour (KNN), were constructed for IR prediction using the training set. Among these models, the RF algorithm had the best predictive performance, showing an accuracy of 80.14%, an AUC of 0.856, and an F1 score of 0.74 in the test set. We embedded three interpretable methods, the permutation feature importance analysis, partial dependence plot (PDP), and Shapley additive explanations (SHAP) in RF model for model interpretation. Urinary Ba, urinary Mo, blood Pb, and blood Cd levels were identified as the main influencers of IR. Within a specific range, urinary Ba (0.56-3.56µg/L) and urinary Mo (1.06-20.25µg/L) levels exhibited the most pronounced upwards trend with the risk of IR, while blood Pb (0.05-2.81µg/dL) and blood Cd (0.24-0.65µg/L) levels showed a declining trend with IR. The findings on the synergistic effects demonstrated that controlling urinary Ba levels might be more crucial for the management of IR. The SHAP decision plot offered personalized care for IR based on heavy metal control. In conclusion, by utilizing interpretable ML approaches, we emphasize the predictive value of heavy metals for IR, especially Ba, Mo, Pb, and Cd.