Sinter is the core raw material for blast furnaces.Flue pressure,which is an important state parameter,affects sinter quality.In this paper,flue pressure prediction and optimization were studied based on the shapley a...Sinter is the core raw material for blast furnaces.Flue pressure,which is an important state parameter,affects sinter quality.In this paper,flue pressure prediction and optimization were studied based on the shapley additive explanation(SHAP)to predict the flue pressure and take targeted adjustment measures.First,the sintering process data were collected and processed.A flue pressure prediction model was then constructed after comparing different feature selection methods and model algorithms using SHAP+extremely random-ized trees(ET).The prediction accuracy of the model within the error range of±0.25 kPa was 92.63%.SHAP analysis was employed to improve the interpretability of the prediction model.The effects of various sintering operation parameters on flue pressure,the relation-ship between the numerical range of key operation parameters and flue pressure,the effect of operation parameter combinations on flue pressure,and the prediction process of the flue pressure prediction model on a single sample were analyzed.A flue pressure optimization module was also constructed and analyzed when the prediction satisfied the judgment conditions.The operating parameter combination was then pushed.The flue pressure was increased by 5.87%during the verification process,achieving a good optimization effect.展开更多
BACKGROUND Colorectal polyps are precancerous diseases of colorectal cancer.Early detection and resection of colorectal polyps can effectively reduce the mortality of colorectal cancer.Endoscopic mucosal resection(EMR...BACKGROUND Colorectal polyps are precancerous diseases of colorectal cancer.Early detection and resection of colorectal polyps can effectively reduce the mortality of colorectal cancer.Endoscopic mucosal resection(EMR)is a common polypectomy proce-dure in clinical practice,but it has a high postoperative recurrence rate.Currently,there is no predictive model for the recurrence of colorectal polyps after EMR.AIM To construct and validate a machine learning(ML)model for predicting the risk of colorectal polyp recurrence one year after EMR.METHODS This study retrospectively collected data from 1694 patients at three medical centers in Xuzhou.Additionally,a total of 166 patients were collected to form a prospective validation set.Feature variable screening was conducted using uni-variate and multivariate logistic regression analyses,and five ML algorithms were used to construct the predictive models.The optimal models were evaluated based on different performance metrics.Decision curve analysis(DCA)and SHapley Additive exPlanation(SHAP)analysis were performed to assess clinical applicability and predictor importance.RESULTS Multivariate logistic regression analysis identified 8 independent risk factors for colorectal polyp recurrence one year after EMR(P<0.05).Among the models,eXtreme Gradient Boosting(XGBoost)demonstrated the highest area under the curve(AUC)in the training set,internal validation set,and prospective validation set,with AUCs of 0.909(95%CI:0.89-0.92),0.921(95%CI:0.90-0.94),and 0.963(95%CI:0.94-0.99),respectively.DCA indicated favorable clinical utility for the XGBoost model.SHAP analysis identified smoking history,family history,and age as the top three most important predictors in the model.CONCLUSION The XGBoost model has the best predictive performance and can assist clinicians in providing individualized colonoscopy follow-up recommendations.展开更多
Mechanical properties are critical to the quality of hot-rolled steel pipe products.Accurately understanding the relationship between rolling parameters and mechanical properties is crucial for effective prediction an...Mechanical properties are critical to the quality of hot-rolled steel pipe products.Accurately understanding the relationship between rolling parameters and mechanical properties is crucial for effective prediction and control.To address this,an industrial big data platform was developed to collect and process multi-source heterogeneous data from the entire production process,providing a complete dataset for mechanical property prediction.The adaptive bandwidth kernel density estimation(ABKDE)method was proposed to adjust bandwidth dynamically based on data density.Combining long short-term memory neural networks with ABKDE offers robust prediction interval capabilities for mechanical properties.The proposed method was deployed in a large-scale steel plant,which demonstrated superior prediction interval performance compared to lower upper bound estimation,mean variance estimation,and extreme learning machine-adaptive bandwidth kernel density estimation,achieving a prediction interval normalized average width of 0.37,a prediction interval coverage probability of 0.94,and the lowest coverage width-based criterion of 1.35.Notably,shapley additive explanations-based explanations significantly improved the proposed model’s credibility by providing a clear analysis of feature impacts.展开更多
Machine learning(ML)models are widely used for predicting undrained shear strength(USS),but interpretability has been a limitation in various studies.Therefore,this study introduced shapley additive explanations(SHAP)...Machine learning(ML)models are widely used for predicting undrained shear strength(USS),but interpretability has been a limitation in various studies.Therefore,this study introduced shapley additive explanations(SHAP)to clarify the contribution of each input feature in USS prediction.Three ML models,artificial neural network(ANN),extreme gradient boosting(XGBoost),and random forest(RF),were employed,with accuracy evaluated using mean squared error,mean absolute error,and coefficient of determination(R^(2)).The RF achieved the highest performance with an R^(2) of 0.82.SHAP analysis identified pre-consolidation stress as a key contributor to USS prediction.SHAP dependence plots reveal that the ANN captures smoother,linear feature-output relationships,while the RF handles complex,non-linear interactions more effectively.This suggests a non-linear relationship between USS and input features,with RF outperforming ANN.These findings highlight SHAP’s role in enhancing interpretability and promoting transparency and reliability in ML predictions for geotechnical applications.展开更多
Predicting molecular properties is essential for advancing for advancing drug discovery and design. Recently, Graph Neural Networks (GNNs) have gained prominence due to their ability to capture the complex structural ...Predicting molecular properties is essential for advancing for advancing drug discovery and design. Recently, Graph Neural Networks (GNNs) have gained prominence due to their ability to capture the complex structural and relational information inherent in molecular graphs. Despite their effectiveness, the “black-box” nature of GNNs remains a significant obstacle to their widespread adoption in chemistry, as it hinders interpretability and trust. In this context, several explanation methods based on factual reasoning have emerged. These methods aim to interpret the predictions made by GNNs by analyzing the key features contributing to the prediction. However, these approaches fail to answer critical questions: “How to ensure that the structure-property mapping learned by GNNs is consistent with established domain knowledge”. In this paper, we propose MMGCF, a novel counterfactual explanation framework designed specifically for the prediction of GNN-based molecular properties. MMGCF constructs a hierarchical tree structure on molecular motifs, enabling the systematic generation of counterfactuals through motif perturbations. This framework identifies causally significant motifs and elucidates their impact on model predictions, offering insights into the relationship between structural modifications and predicted properties. Our method demonstrates its effectiveness through comprehensive quantitative and qualitative evaluations of four real-world molecular datasets.展开更多
The Titanic sunk 113 years ago on April 14-15,after hitting an iceberg,with human error likely causing the ship to wander into those dangerous waters.Today,autonomous systems built on AI can help ships avoid such acci...The Titanic sunk 113 years ago on April 14-15,after hitting an iceberg,with human error likely causing the ship to wander into those dangerous waters.Today,autonomous systems built on AI can help ships avoid such accidents.But could such a system explain to the captain why it was controlling the ship in a certain way?展开更多
In this study,we used the Kolmogorov-Arnold networks(KAN)model based on the Kolmogorov-Arnold representation theorem for a comprehensive and fair evaluation.We compare its performance with four other powerful classifi...In this study,we used the Kolmogorov-Arnold networks(KAN)model based on the Kolmogorov-Arnold representation theorem for a comprehensive and fair evaluation.We compare its performance with four other powerful classification models across three datasets:a simple slope binary classification dataset,an imbalanced rockburst dataset,and a highly discrete liquefaction dataset.First,a thorough review of machine-learning algorithms for geohazard assessment was conducted.Subsequently,three datasets were collected from real engineering practices,and their data structures were visualized.Bayesian optimization was then used to adjust the parameters of all models across all datasets.To ensure model interpretability,a global sensitivity analysis based on Sobol indices was performed,establishing an interpretable visual analysis of the model's decision-making process.For a fair evaluation,various metrics and repeated stratified 10-fold cross-validation were employed to comprehensively analyze the predictive results of the models.The results indicate that although the KAN model,based on the RBF kernel,achieves the expected performance on the binary classification dataset,it also performs well on imbalanced and highly discrete datasets,significantly surpassing other commonly used classification models.This demonstrated the broad application potential of the KAN model in geotechnical engineering.展开更多
BACKGROUND Parastomal hernia(PSH)is a common and challenging complication following preventive ostomy in rectal cancer patients,lacking accurate tools for early risk prediction.AIM To explore the application of machin...BACKGROUND Parastomal hernia(PSH)is a common and challenging complication following preventive ostomy in rectal cancer patients,lacking accurate tools for early risk prediction.AIM To explore the application of machine learning algorithms in predicting the occurrence of PSH in patients undergoing preventive ostomy after rectal cancer resection,providing valuable support for clinical decision-making.METHODS A retrospective analysis was conducted on the clinical data of 579 patients who underwent rectal cancer resection with preventive ostomy at Tongji Hospital,Huazhong University of Science and Technology,between January 2015 and June 2023.Various machine learning models were constructed and trained using preoperative and intraoperative clinical variables to assess their predictive performance for PSH risk.SHapley Additive exPlanations(SHAP)were used to analyze the importance of features in the models.RESULTS A total of 579 patients were included,with 31(5.3%)developing PSH.Among the machine learning models,the random forest(RF)model showed the best performance.In the test set,the RF model achieved an area under the curve of 0.900,sensitivity of 0.900,and specificity of 0.725.SHAP analysis revealed that tumor distance from the anal verge,body mass index,and preoperative hypertension were the key factors influencing the occurrence of PSH.CONCLUSION Machine learning,particularly the RF model,demonstrates high accuracy and reliability in predicting PSH after preventive ostomy in rectal cancer patients.This technology supports personalized risk assessment and postoperative management,showing significant potential for clinical application.An online predictive platform based on the RF model(https://yangsu2023.shinyapps.io/parastomal_hernia/)has been developed to assist in early screening and intervention for high-risk patients,further enhancing postoperative management and improving patients’quality of life.展开更多
Although substantial research shows the effectiveness of written corrective feedback(WCF)in treating simple grammar structures,more research is still needed to refute Truscott’s claim that WCF may not work on complex...Although substantial research shows the effectiveness of written corrective feedback(WCF)in treating simple grammar structures,more research is still needed to refute Truscott’s claim that WCF may not work on complex grammar structures.Similarly,a previous body of research has shown that the degree of explicitness of feedback moderates the efficacy of WCF.However,most WCF studies have systematically manipulated only direct corrective feedback.The current study was therefore conducted to fill these gaps in the literature.To this end,five intact classes of Functional English were recruited and later randomly assigned to four treatment groups:DCF,DCF+ME,ICF,and ICF+ME,and one control group that received no feedback.All the groups took part in three WCF treatment sessions,during which they wrote two different pieces:a news report and a picture description.Later,only the treatment groups received the WCF.The WCF’s effectiveness was measured by writing tests and grammaticality judgment tasks(GJT).The results demonstrated that WCF helped L2 learners improve their grammatical accuracy of passive voice tenses.The study further showed that the group that received the most explicit type of WCF fared better than the ones that received the least explicit type of WCF.Important pedagogical implications for ESL/EFL teachers are discussed.展开更多
Accurate reservoir permeability determination is crucial in hydrocarbon exploration and production.Conventional methods relying on empirical correlations and assumptions often result in high costs,time consumption,ina...Accurate reservoir permeability determination is crucial in hydrocarbon exploration and production.Conventional methods relying on empirical correlations and assumptions often result in high costs,time consumption,inaccuracies,and uncertainties.This study introduces a novel hybrid machine learning approach to predict the permeability of the Wangkwar formation in the Gunya oilfield,Northwestern Uganda.The group method of data handling with differential evolution(GMDH-DE)algorithm was used to predict permeability due to its capability to manage complex,nonlinear relationships between variables,reduced computation time,and parameter optimization through evolutionary algorithms.Using 1953 samples from Gunya-1 and Gunya-2 wells for training and 1563 samples from Gunya-3 for testing,the GMDH-DE outperformed the group method of data handling(GMDH)and random forest(RF)in predicting permeability with higher accuracy and lower computation time.The GMDH-DE achieved an R^(2)of 0.9985,RMSE of 3.157,MAE of 2.366,and ME of 0.001 during training,and for testing,the ME,MAE,RMSE,and R^(2)were 1.3508,12.503,21.3898,and 0.9534,respectively.Additionally,the GMDH-DE demonstrated a 41%reduction in processing time compared to GMDH and RF.The model was also used to predict the permeability of the Mita Gamma well in the Mandawa basin,Tanzania,which lacks core data.Shapley additive explanations(SHAP)analysis identified thermal neutron porosity(TNPH),effective porosity(PHIE),and spectral gamma-ray(SGR)as the most critical parameters in permeability prediction.Therefore,the GMDH-DE model offers a novel,efficient,and accurate approach for fast permeability prediction,enhancing hydrocarbon exploration and production.展开更多
Ensuring independent mobility for older adults has become a public health and social concern in China owing to its rapidly aging population.To explore independent mobility trends among older adults and the impact of s...Ensuring independent mobility for older adults has become a public health and social concern in China owing to its rapidly aging population.To explore independent mobility trends among older adults and the impact of sociodemo-graphic characteristics in recent years,this study used data from the Chinese Longitudinal Healthy Longevity Survey from 2012 to 2018,combined with binomial logit regression and CatBoost-Shapley additive explanation(SHAP)method to analyze the relationship between independent mobility and sociodemographic characteristics under bus and walking-oriented environments.Study findings indicated that age and gender significantly affected the independent mobility of older adults.Policymaking should prioritize the needs of older adults,focusing on age and gender differ-ences.Additionally,living expense adequacy significantly influenced independent mobility.Policies should substan-tially support economically disadvantaged older adults,en-suring their basic needs are met through subsidies and other measures.Moreover,the study found a notable impact of widowhood on independent mobility,suggesting enhanced social care and mental health support for widowed older adults,especially those who are long-lived.The outcomes of this study provided evidence for policymakers,which are beneficial for developing elderly-friendly travel policies to ensure and enhance the quality of life and independent mo-bility of older adults.展开更多
BACKGROUND Diabetic foot ulcer(DFU)is a serious and destructive complication of diabetes,which has a high amputation rate and carries a huge social burden.Early detection of risk factors and intervention are essential...BACKGROUND Diabetic foot ulcer(DFU)is a serious and destructive complication of diabetes,which has a high amputation rate and carries a huge social burden.Early detection of risk factors and intervention are essential to reduce amputation rates.With the development of artificial intelligence technology,efficient interpretable predictive models can be generated in clinical practice to improve DFU care.AIM To develop and validate an interpretable model for predicting amputation risk in DFU patients.METHODS This retrospective study collected basic data from 599 patients with DFU in Beijing Shijitan Hospital between January 2015 and June 2024.The data set was randomly divided into a training set and test set with fivefold cross-validation.Three binary variable models were built with the eXtreme Gradient Boosting(XGBoost)algorithm to input risk factors that predict amputation probability.The model performance was optimized by adjusting the super parameters.The pre-dictive performance of the three models was expressed by sensitivity,specificity,positive predictive value,negative predictive value and area under the curve(AUC).Visualization of the prediction results was realized through SHapley Additive exPlanation(SHAP).RESULTS A total of 157(26.2%)patients underwent minor amputation during hospitalization and 50(8.3%)had major amputation.All three XGBoost models demonstrated good discriminative ability,with AUC values>0.7.The model for predicting major amputation achieved the highest performance[AUC=0.977,95%confidence interval(CI):0.956-0.998],followed by the minor amputation model(AUC=0.800,95%CI:0.762-0.838)and the non-amputation model(AUC=0.772,95%CI:0.730-0.814).Feature importance ranking of the three models revealed the risk factors for minor and major amputation.Wagner grade 4/5,osteomyelitis,and high C-reactive protein were all considered important predictive variables.CONCLUSION XGBoost effectively predicts diabetic foot amputation risk and provides interpretable insights to support person-alized treatment decisions.展开更多
Deep learning models have become a core technological tool in the field of medical image analysis.However,these models often suffer from a lack of transparency in their decision-making processes,leading to challenges ...Deep learning models have become a core technological tool in the field of medical image analysis.However,these models often suffer from a lack of transparency in their decision-making processes,leading to challenges related to trust and interpret ability in clinical applications.To address this issue,explainable artificial intelligence(XAI)techniques have been applied to medical image analysis.While showing promising potential,XAI also brings significant ethical risks in practice—most notably,the problem of spurious explanations.Such explanations may rise further concerns regarding patient privacy,data security,and the attribution of decisionmaking authority in medical contexts.This paper analyzes the application of XAI methods—particularly saliency aps—in medical image interpretation,identifies the underlying causes of spurious explanations,and proposes possible mitigation strategies.The aim is to contribute to the responsible and sustainable integration of explainable AI into clinical practice.展开更多
The liver is a crucial gland and the second-largest organ in the human body and also essential in digestion,metabolism,detoxification,and immunity.Liver diseases result from factors such as viral infections,obesity,al...The liver is a crucial gland and the second-largest organ in the human body and also essential in digestion,metabolism,detoxification,and immunity.Liver diseases result from factors such as viral infections,obesity,alcohol consumption,injuries,or genetic predispositions.Pose significant health risks and demand timely diagnosis and treatment to enhance survival rates.Traditionally,diagnosing liver diseases relied heavily on clinical expertise,often leading to subjective,challenging,and time-intensive processes.However,early detection is essential for effective intervention,and advancements in machine learning(ML)have demonstrated remarkable success in predicting various conditions,including Chronic Obstructive Pulmonary Disease(COPD),hypertension,and diabetes.This study proposed a novel XGBoost-liver predictor by integrating distinct feature methodologies,including Ranking and Statistical Projection-based strategies to detect early signs of liver disease.The Fisher score method is applied to perform global interpretation analysis,helping to select optimal features by assessing their contributions to the overall model.The performance of the proposed model has been extensively evaluated through k-fold cross-validation tests.Firstly,the performance of the proposed model is evaluated using individual and hybrid features.Secondly,the XGBoost-Liver model performance is compared to that of commonly used classifier algorithms.Thirdly,its performance is compared with the existing state-of-the-art computational models.The experimental results show that the proposed model performed better than the existing predictors,reaching an average accuracy rate of 92.07%.This paper demonstrates the potential of machine learning to improve liver disease prediction,enhance diagnostic accuracy,and enable timely medical interventions for better patient outcomes.展开更多
基金supported by the General Program of the National Natural Science Foundation of China(No.52274326)the China Baowu Low Carbon Metallurgy Innovation Foundation(No.BWLCF202109)the Seventh Batch of Ten Thousand Talents Plan of China(No.ZX20220553).
文摘Sinter is the core raw material for blast furnaces.Flue pressure,which is an important state parameter,affects sinter quality.In this paper,flue pressure prediction and optimization were studied based on the shapley additive explanation(SHAP)to predict the flue pressure and take targeted adjustment measures.First,the sintering process data were collected and processed.A flue pressure prediction model was then constructed after comparing different feature selection methods and model algorithms using SHAP+extremely random-ized trees(ET).The prediction accuracy of the model within the error range of±0.25 kPa was 92.63%.SHAP analysis was employed to improve the interpretability of the prediction model.The effects of various sintering operation parameters on flue pressure,the relation-ship between the numerical range of key operation parameters and flue pressure,the effect of operation parameter combinations on flue pressure,and the prediction process of the flue pressure prediction model on a single sample were analyzed.A flue pressure optimization module was also constructed and analyzed when the prediction satisfied the judgment conditions.The operating parameter combination was then pushed.The flue pressure was increased by 5.87%during the verification process,achieving a good optimization effect.
文摘BACKGROUND Colorectal polyps are precancerous diseases of colorectal cancer.Early detection and resection of colorectal polyps can effectively reduce the mortality of colorectal cancer.Endoscopic mucosal resection(EMR)is a common polypectomy proce-dure in clinical practice,but it has a high postoperative recurrence rate.Currently,there is no predictive model for the recurrence of colorectal polyps after EMR.AIM To construct and validate a machine learning(ML)model for predicting the risk of colorectal polyp recurrence one year after EMR.METHODS This study retrospectively collected data from 1694 patients at three medical centers in Xuzhou.Additionally,a total of 166 patients were collected to form a prospective validation set.Feature variable screening was conducted using uni-variate and multivariate logistic regression analyses,and five ML algorithms were used to construct the predictive models.The optimal models were evaluated based on different performance metrics.Decision curve analysis(DCA)and SHapley Additive exPlanation(SHAP)analysis were performed to assess clinical applicability and predictor importance.RESULTS Multivariate logistic regression analysis identified 8 independent risk factors for colorectal polyp recurrence one year after EMR(P<0.05).Among the models,eXtreme Gradient Boosting(XGBoost)demonstrated the highest area under the curve(AUC)in the training set,internal validation set,and prospective validation set,with AUCs of 0.909(95%CI:0.89-0.92),0.921(95%CI:0.90-0.94),and 0.963(95%CI:0.94-0.99),respectively.DCA indicated favorable clinical utility for the XGBoost model.SHAP analysis identified smoking history,family history,and age as the top three most important predictors in the model.CONCLUSION The XGBoost model has the best predictive performance and can assist clinicians in providing individualized colonoscopy follow-up recommendations.
基金supported by the National Key Research and Development Plan(Grant No.2023YFB3712400)the National Key Research and Development Plan(Grant No.2020YFB1713600).
文摘Mechanical properties are critical to the quality of hot-rolled steel pipe products.Accurately understanding the relationship between rolling parameters and mechanical properties is crucial for effective prediction and control.To address this,an industrial big data platform was developed to collect and process multi-source heterogeneous data from the entire production process,providing a complete dataset for mechanical property prediction.The adaptive bandwidth kernel density estimation(ABKDE)method was proposed to adjust bandwidth dynamically based on data density.Combining long short-term memory neural networks with ABKDE offers robust prediction interval capabilities for mechanical properties.The proposed method was deployed in a large-scale steel plant,which demonstrated superior prediction interval performance compared to lower upper bound estimation,mean variance estimation,and extreme learning machine-adaptive bandwidth kernel density estimation,achieving a prediction interval normalized average width of 0.37,a prediction interval coverage probability of 0.94,and the lowest coverage width-based criterion of 1.35.Notably,shapley additive explanations-based explanations significantly improved the proposed model’s credibility by providing a clear analysis of feature impacts.
基金Ho Chi Minh City University of Technology (HCMUT), VNU-HCM for supporting this study
文摘Machine learning(ML)models are widely used for predicting undrained shear strength(USS),but interpretability has been a limitation in various studies.Therefore,this study introduced shapley additive explanations(SHAP)to clarify the contribution of each input feature in USS prediction.Three ML models,artificial neural network(ANN),extreme gradient boosting(XGBoost),and random forest(RF),were employed,with accuracy evaluated using mean squared error,mean absolute error,and coefficient of determination(R^(2)).The RF achieved the highest performance with an R^(2) of 0.82.SHAP analysis identified pre-consolidation stress as a key contributor to USS prediction.SHAP dependence plots reveal that the ANN captures smoother,linear feature-output relationships,while the RF handles complex,non-linear interactions more effectively.This suggests a non-linear relationship between USS and input features,with RF outperforming ANN.These findings highlight SHAP’s role in enhancing interpretability and promoting transparency and reliability in ML predictions for geotechnical applications.
文摘Predicting molecular properties is essential for advancing for advancing drug discovery and design. Recently, Graph Neural Networks (GNNs) have gained prominence due to their ability to capture the complex structural and relational information inherent in molecular graphs. Despite their effectiveness, the “black-box” nature of GNNs remains a significant obstacle to their widespread adoption in chemistry, as it hinders interpretability and trust. In this context, several explanation methods based on factual reasoning have emerged. These methods aim to interpret the predictions made by GNNs by analyzing the key features contributing to the prediction. However, these approaches fail to answer critical questions: “How to ensure that the structure-property mapping learned by GNNs is consistent with established domain knowledge”. In this paper, we propose MMGCF, a novel counterfactual explanation framework designed specifically for the prediction of GNN-based molecular properties. MMGCF constructs a hierarchical tree structure on molecular motifs, enabling the systematic generation of counterfactuals through motif perturbations. This framework identifies causally significant motifs and elucidates their impact on model predictions, offering insights into the relationship between structural modifications and predicted properties. Our method demonstrates its effectiveness through comprehensive quantitative and qualitative evaluations of four real-world molecular datasets.
文摘The Titanic sunk 113 years ago on April 14-15,after hitting an iceberg,with human error likely causing the ship to wander into those dangerous waters.Today,autonomous systems built on AI can help ships avoid such accidents.But could such a system explain to the captain why it was controlling the ship in a certain way?
基金supported by the National Natural Science Foundation of China(Grant Nos.42107214 and 42477157).
文摘In this study,we used the Kolmogorov-Arnold networks(KAN)model based on the Kolmogorov-Arnold representation theorem for a comprehensive and fair evaluation.We compare its performance with four other powerful classification models across three datasets:a simple slope binary classification dataset,an imbalanced rockburst dataset,and a highly discrete liquefaction dataset.First,a thorough review of machine-learning algorithms for geohazard assessment was conducted.Subsequently,three datasets were collected from real engineering practices,and their data structures were visualized.Bayesian optimization was then used to adjust the parameters of all models across all datasets.To ensure model interpretability,a global sensitivity analysis based on Sobol indices was performed,establishing an interpretable visual analysis of the model's decision-making process.For a fair evaluation,various metrics and repeated stratified 10-fold cross-validation were employed to comprehensively analyze the predictive results of the models.The results indicate that although the KAN model,based on the RBF kernel,achieves the expected performance on the binary classification dataset,it also performs well on imbalanced and highly discrete datasets,significantly surpassing other commonly used classification models.This demonstrated the broad application potential of the KAN model in geotechnical engineering.
文摘BACKGROUND Parastomal hernia(PSH)is a common and challenging complication following preventive ostomy in rectal cancer patients,lacking accurate tools for early risk prediction.AIM To explore the application of machine learning algorithms in predicting the occurrence of PSH in patients undergoing preventive ostomy after rectal cancer resection,providing valuable support for clinical decision-making.METHODS A retrospective analysis was conducted on the clinical data of 579 patients who underwent rectal cancer resection with preventive ostomy at Tongji Hospital,Huazhong University of Science and Technology,between January 2015 and June 2023.Various machine learning models were constructed and trained using preoperative and intraoperative clinical variables to assess their predictive performance for PSH risk.SHapley Additive exPlanations(SHAP)were used to analyze the importance of features in the models.RESULTS A total of 579 patients were included,with 31(5.3%)developing PSH.Among the machine learning models,the random forest(RF)model showed the best performance.In the test set,the RF model achieved an area under the curve of 0.900,sensitivity of 0.900,and specificity of 0.725.SHAP analysis revealed that tumor distance from the anal verge,body mass index,and preoperative hypertension were the key factors influencing the occurrence of PSH.CONCLUSION Machine learning,particularly the RF model,demonstrates high accuracy and reliability in predicting PSH after preventive ostomy in rectal cancer patients.This technology supports personalized risk assessment and postoperative management,showing significant potential for clinical application.An online predictive platform based on the RF model(https://yangsu2023.shinyapps.io/parastomal_hernia/)has been developed to assist in early screening and intervention for high-risk patients,further enhancing postoperative management and improving patients’quality of life.
文摘Although substantial research shows the effectiveness of written corrective feedback(WCF)in treating simple grammar structures,more research is still needed to refute Truscott’s claim that WCF may not work on complex grammar structures.Similarly,a previous body of research has shown that the degree of explicitness of feedback moderates the efficacy of WCF.However,most WCF studies have systematically manipulated only direct corrective feedback.The current study was therefore conducted to fill these gaps in the literature.To this end,five intact classes of Functional English were recruited and later randomly assigned to four treatment groups:DCF,DCF+ME,ICF,and ICF+ME,and one control group that received no feedback.All the groups took part in three WCF treatment sessions,during which they wrote two different pieces:a news report and a picture description.Later,only the treatment groups received the WCF.The WCF’s effectiveness was measured by writing tests and grammaticality judgment tasks(GJT).The results demonstrated that WCF helped L2 learners improve their grammatical accuracy of passive voice tenses.The study further showed that the group that received the most explicit type of WCF fared better than the ones that received the least explicit type of WCF.Important pedagogical implications for ESL/EFL teachers are discussed.
基金supported by the Major National Science and Technology Programs in the“Thirteenth Five-Year”Plan period(Grant No.2017ZX05032-002-004)the Innovation Team Funding of Natural Science Foundation of Hubei Province,China(Grant No.2021CFA031)the Chinese Scholarship Council(CSC)and Silk Road Institute for their support in terms of stipend.
文摘Accurate reservoir permeability determination is crucial in hydrocarbon exploration and production.Conventional methods relying on empirical correlations and assumptions often result in high costs,time consumption,inaccuracies,and uncertainties.This study introduces a novel hybrid machine learning approach to predict the permeability of the Wangkwar formation in the Gunya oilfield,Northwestern Uganda.The group method of data handling with differential evolution(GMDH-DE)algorithm was used to predict permeability due to its capability to manage complex,nonlinear relationships between variables,reduced computation time,and parameter optimization through evolutionary algorithms.Using 1953 samples from Gunya-1 and Gunya-2 wells for training and 1563 samples from Gunya-3 for testing,the GMDH-DE outperformed the group method of data handling(GMDH)and random forest(RF)in predicting permeability with higher accuracy and lower computation time.The GMDH-DE achieved an R^(2)of 0.9985,RMSE of 3.157,MAE of 2.366,and ME of 0.001 during training,and for testing,the ME,MAE,RMSE,and R^(2)were 1.3508,12.503,21.3898,and 0.9534,respectively.Additionally,the GMDH-DE demonstrated a 41%reduction in processing time compared to GMDH and RF.The model was also used to predict the permeability of the Mita Gamma well in the Mandawa basin,Tanzania,which lacks core data.Shapley additive explanations(SHAP)analysis identified thermal neutron porosity(TNPH),effective porosity(PHIE),and spectral gamma-ray(SGR)as the most critical parameters in permeability prediction.Therefore,the GMDH-DE model offers a novel,efficient,and accurate approach for fast permeability prediction,enhancing hydrocarbon exploration and production.
基金The National Natural Science Foundation of China (No. 52272367)the Natural Science Foundation of Jiangsu Province (No. BK20231324)。
文摘Ensuring independent mobility for older adults has become a public health and social concern in China owing to its rapidly aging population.To explore independent mobility trends among older adults and the impact of sociodemo-graphic characteristics in recent years,this study used data from the Chinese Longitudinal Healthy Longevity Survey from 2012 to 2018,combined with binomial logit regression and CatBoost-Shapley additive explanation(SHAP)method to analyze the relationship between independent mobility and sociodemographic characteristics under bus and walking-oriented environments.Study findings indicated that age and gender significantly affected the independent mobility of older adults.Policymaking should prioritize the needs of older adults,focusing on age and gender differ-ences.Additionally,living expense adequacy significantly influenced independent mobility.Policies should substan-tially support economically disadvantaged older adults,en-suring their basic needs are met through subsidies and other measures.Moreover,the study found a notable impact of widowhood on independent mobility,suggesting enhanced social care and mental health support for widowed older adults,especially those who are long-lived.The outcomes of this study provided evidence for policymakers,which are beneficial for developing elderly-friendly travel policies to ensure and enhance the quality of life and independent mo-bility of older adults.
文摘BACKGROUND Diabetic foot ulcer(DFU)is a serious and destructive complication of diabetes,which has a high amputation rate and carries a huge social burden.Early detection of risk factors and intervention are essential to reduce amputation rates.With the development of artificial intelligence technology,efficient interpretable predictive models can be generated in clinical practice to improve DFU care.AIM To develop and validate an interpretable model for predicting amputation risk in DFU patients.METHODS This retrospective study collected basic data from 599 patients with DFU in Beijing Shijitan Hospital between January 2015 and June 2024.The data set was randomly divided into a training set and test set with fivefold cross-validation.Three binary variable models were built with the eXtreme Gradient Boosting(XGBoost)algorithm to input risk factors that predict amputation probability.The model performance was optimized by adjusting the super parameters.The pre-dictive performance of the three models was expressed by sensitivity,specificity,positive predictive value,negative predictive value and area under the curve(AUC).Visualization of the prediction results was realized through SHapley Additive exPlanation(SHAP).RESULTS A total of 157(26.2%)patients underwent minor amputation during hospitalization and 50(8.3%)had major amputation.All three XGBoost models demonstrated good discriminative ability,with AUC values>0.7.The model for predicting major amputation achieved the highest performance[AUC=0.977,95%confidence interval(CI):0.956-0.998],followed by the minor amputation model(AUC=0.800,95%CI:0.762-0.838)and the non-amputation model(AUC=0.772,95%CI:0.730-0.814).Feature importance ranking of the three models revealed the risk factors for minor and major amputation.Wagner grade 4/5,osteomyelitis,and high C-reactive protein were all considered important predictive variables.CONCLUSION XGBoost effectively predicts diabetic foot amputation risk and provides interpretable insights to support person-alized treatment decisions.
文摘Deep learning models have become a core technological tool in the field of medical image analysis.However,these models often suffer from a lack of transparency in their decision-making processes,leading to challenges related to trust and interpret ability in clinical applications.To address this issue,explainable artificial intelligence(XAI)techniques have been applied to medical image analysis.While showing promising potential,XAI also brings significant ethical risks in practice—most notably,the problem of spurious explanations.Such explanations may rise further concerns regarding patient privacy,data security,and the attribution of decisionmaking authority in medical contexts.This paper analyzes the application of XAI methods—particularly saliency aps—in medical image interpretation,identifies the underlying causes of spurious explanations,and proposes possible mitigation strategies.The aim is to contribute to the responsible and sustainable integration of explainable AI into clinical practice.
基金supported by Research Supporting Project Number(RSPD2025R585),King Saud University,Riyadh,Saudi Arabia.
文摘The liver is a crucial gland and the second-largest organ in the human body and also essential in digestion,metabolism,detoxification,and immunity.Liver diseases result from factors such as viral infections,obesity,alcohol consumption,injuries,or genetic predispositions.Pose significant health risks and demand timely diagnosis and treatment to enhance survival rates.Traditionally,diagnosing liver diseases relied heavily on clinical expertise,often leading to subjective,challenging,and time-intensive processes.However,early detection is essential for effective intervention,and advancements in machine learning(ML)have demonstrated remarkable success in predicting various conditions,including Chronic Obstructive Pulmonary Disease(COPD),hypertension,and diabetes.This study proposed a novel XGBoost-liver predictor by integrating distinct feature methodologies,including Ranking and Statistical Projection-based strategies to detect early signs of liver disease.The Fisher score method is applied to perform global interpretation analysis,helping to select optimal features by assessing their contributions to the overall model.The performance of the proposed model has been extensively evaluated through k-fold cross-validation tests.Firstly,the performance of the proposed model is evaluated using individual and hybrid features.Secondly,the XGBoost-Liver model performance is compared to that of commonly used classifier algorithms.Thirdly,its performance is compared with the existing state-of-the-art computational models.The experimental results show that the proposed model performed better than the existing predictors,reaching an average accuracy rate of 92.07%.This paper demonstrates the potential of machine learning to improve liver disease prediction,enhance diagnostic accuracy,and enable timely medical interventions for better patient outcomes.
