首页|Chengdu University of Technology Reports Findings in Liver Diseases and Conditio ns (Polynomial-SHAP analysis of liver disease markers for capturing of complex f eature interactions in machine learning models)
Chengdu University of Technology Reports Findings in Liver Diseases and Conditio ns (Polynomial-SHAP analysis of liver disease markers for capturing of complex f eature interactions in machine learning models)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – New research on Liver Diseases and Conditions is the subject of a report. According to news reporting originating from Chengdu, P eople’s Republic of China, by NewsRx correspondents, research stated, “Liver dis ease diagnosis is pivotal for effective patient management, and machine learning techniques have shown promise in this domain. In this study, we investigate the impact of Polynomial-SHapley Additive exPlanations analysis on enhancing the pe rformance and interpretability of machine learning models for liver disease clas sification.” Our news editors obtained a quote from the research from the Chengdu University of Technology, “Our results demonstrate significant improvements in accuracy, pr ecision, recall, F1_score, and Matthews correlation coefficient acr oss various algorithms when polynomial- SHapley Additive exPlanations analysis i s applied. Specifically, the Light Gradient Boosting Machine model achieves exce ptional performance with 100 % accuracy in both scenarios. Further more, by comparing the results obtained with and without the approach, we observ e substantial differences in the performance, highlighting the importance of inc orporating Polynomial-SHapley Additive exPlanations analysis for improved model performance. The Polynomial features and SHapley Additive exPlanations values al so enhance the interpretability of machine learning models by capturing complex feature interactions, enabling users to gain deeper insights into the underlying mechanisms driving the diagnosis. Moreover, data rebalancing using Synthetic Mi nority Oversampling Technique and parameter tuning were employed to optimize th e performance of the models.”
ChengduPeople’s Republic of ChinaAsi aCyborgsDigestive System Diseases and ConditionsEmerging TechnologiesHea lth and MedicineLiver Diseases and ConditionsMachine LearningMathematicsPolynomial
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