Background:Liver disease(LD)significantly impacts global health,requiring accurate diagnostic methods.This study aims to develop an automated system for LD prediction using machine learning(ML)and explainable artifici...Background:Liver disease(LD)significantly impacts global health,requiring accurate diagnostic methods.This study aims to develop an automated system for LD prediction using machine learning(ML)and explainable artificial intelligence(XAI),enhancing diagnostic precision and interpretability.Methods:This research systematically analyzes two distinct datasets encompassing liver health indicators.A combination of preprocessing techniques,including feature optimization methods such as Forward Feature Selection(FFS),Backward Feature Selection(BFS),and Recursive Feature Elimination(RFE),is applied to enhance data quality.After that,ML models,namely Support Vector Machines(SVM),Naive Bayes(NB),Random Forest(RF),K-nearest neighbors(KNN),Decision Trees(DT),and a novel Tree Selection and Stacking Ensemble-based RF(TSRF),are assessed in the dataset to diagnose LD.Finally,the ultimate model is selected based on incorporating cross-validation and evaluation through performance metrics like accuracy,precision,specificity,etc.,and efficient XAI methods express the ultimate model’s interoperability.Findings:The analysis reveals TSRF as the most effective model,achieving a peak accuracy of 99.92%on Dataset-1 without feature optimization and 88.88%on Dataset-2 with RFE optimization.XAI techniques,including SHAP and LIME plots,highlight key features influencing model predictions,providing insights into the reasoning behind classification outcomes.Interpretation:The findings highlight TSRF’s potential in improving LD diagnosis,using XAI to enhance transparency and trust in ML models.Despite high accuracy and interpretability,limitations such as dataset bias and lack of clinical validation remain.Future work focuses on integrating advanced XAI,diversifying datasets,and applying the approach in clinical settings for reliable diagnostics.展开更多
An experimental investigation was made into three-dimensional separated flow and the vortices within the flow separation in a decelerating channel flow generated by the suction from a porous side wall. The flows along...An experimental investigation was made into three-dimensional separated flow and the vortices within the flow separation in a decelerating channel flow generated by the suction from a porous side wall. The flows along the side and bottom walls were visualized by the surface tuft method. The turbulent internal flow was measured by the split-film probe to investigate the turbulent flow including the reverse flow. In the flow visualization for the strong decelerating flow （the suction flow ratio：0.8）, two typical flow patterns appear alternatively. One is that the flow near the bottom wall separates more upstream than the flow near the top wall and a clockwise vortex can be seen in the separation region. Another is the reversal flow pattern with a counterclockwise vortex. By the turbulent flow measurement using the split-film probe, two peaks of turbulence level are observed for the strong decelerating flow case. These peaks can be related with two flow patterns mentioned above.展开更多
文摘Background:Liver disease(LD)significantly impacts global health,requiring accurate diagnostic methods.This study aims to develop an automated system for LD prediction using machine learning(ML)and explainable artificial intelligence(XAI),enhancing diagnostic precision and interpretability.Methods:This research systematically analyzes two distinct datasets encompassing liver health indicators.A combination of preprocessing techniques,including feature optimization methods such as Forward Feature Selection(FFS),Backward Feature Selection(BFS),and Recursive Feature Elimination(RFE),is applied to enhance data quality.After that,ML models,namely Support Vector Machines(SVM),Naive Bayes(NB),Random Forest(RF),K-nearest neighbors(KNN),Decision Trees(DT),and a novel Tree Selection and Stacking Ensemble-based RF(TSRF),are assessed in the dataset to diagnose LD.Finally,the ultimate model is selected based on incorporating cross-validation and evaluation through performance metrics like accuracy,precision,specificity,etc.,and efficient XAI methods express the ultimate model’s interoperability.Findings:The analysis reveals TSRF as the most effective model,achieving a peak accuracy of 99.92%on Dataset-1 without feature optimization and 88.88%on Dataset-2 with RFE optimization.XAI techniques,including SHAP and LIME plots,highlight key features influencing model predictions,providing insights into the reasoning behind classification outcomes.Interpretation:The findings highlight TSRF’s potential in improving LD diagnosis,using XAI to enhance transparency and trust in ML models.Despite high accuracy and interpretability,limitations such as dataset bias and lack of clinical validation remain.Future work focuses on integrating advanced XAI,diversifying datasets,and applying the approach in clinical settings for reliable diagnostics.
文摘An experimental investigation was made into three-dimensional separated flow and the vortices within the flow separation in a decelerating channel flow generated by the suction from a porous side wall. The flows along the side and bottom walls were visualized by the surface tuft method. The turbulent internal flow was measured by the split-film probe to investigate the turbulent flow including the reverse flow. In the flow visualization for the strong decelerating flow （the suction flow ratio：0.8）, two typical flow patterns appear alternatively. One is that the flow near the bottom wall separates more upstream than the flow near the top wall and a clockwise vortex can be seen in the separation region. Another is the reversal flow pattern with a counterclockwise vortex. By the turbulent flow measurement using the split-film probe, two peaks of turbulence level are observed for the strong decelerating flow case. These peaks can be related with two flow patterns mentioned above.
