Cardiovascular Diseases (CVDs) pose a significant global health challenge, necessitating accurate risk prediction for effective preventive measures. This comprehensive comparative study explores the performance of tra...Cardiovascular Diseases (CVDs) pose a significant global health challenge, necessitating accurate risk prediction for effective preventive measures. This comprehensive comparative study explores the performance of traditional Machine Learning (ML) and Deep Learning (DL) models in predicting CVD risk, utilizing a meticulously curated dataset derived from health records. Rigorous preprocessing, including normalization and outlier removal, enhances model robustness. Diverse ML models (Logistic Regression, Random Forest, Support Vector Machine, K-Nearest Neighbor, Decision Tree, and Gradient Boosting) are compared with a Long Short-Term Memory (LSTM) neural network for DL. Evaluation metrics include accuracy, ROC AUC, computation time, and memory usage. Results identify the Gradient Boosting Classifier and LSTM as top performers, demonstrating high accuracy and ROC AUC scores. Comparative analyses highlight model strengths and limitations, contributing valuable insights for optimizing predictive strategies. This study advances predictive analytics for cardiovascular health, with implications for personalized medicine. The findings underscore the versatility of intelligent systems in addressing health challenges, emphasizing the broader applications of ML and DL in disease identification beyond cardiovascular health.展开更多
Assisted by framework of multimedia total exposure model for hazard waste sites(CalTOX),potential influences of scenario-uncertainty on multimedia health risk assessment(MHRA) and decision-making were quantitatively a...Assisted by framework of multimedia total exposure model for hazard waste sites(CalTOX),potential influences of scenario-uncertainty on multimedia health risk assessment(MHRA) and decision-making were quantitatively analyzed in a primary extent under the Chinese scenario case by deliberately varying the two key scenario-elements,namely conceptual exposure pathways combination and aim receptor cohorts choice.Results show that the independent change of one exposure pathway or receptor cohort could lead variation of MHRA results in the range of 3.6×10-6-1.4×10-5 or 6.7×10-6-2.3×10-5.And randomly simultaneous change of those two elements could lead variation of MHRA results at the range of 7.7×10-8-2.3×10-5.On the basis of the corresponding sensitivity analysis,pathways which made a valid contribution to the final modeling risk value occupied only 16.7% of all considered pathways.Afterwards,comparative analysis between influence of parameter-uncertainty and influence of scenario-uncertainty was made.In consideration of interrelationship among all types of uncertainties and financial reasonability during MHRA procedures,the integrated method how to optimize the entire procedures of MHRA was presented innovatively based on sensitivity analysis,scenario-discussion and nest Monte Carlo simulation or fuzzy mathematics.展开更多
To assess the operational safety risk of long-term evolution for the metro(LTE-M)communication system more accurately,the guide maintenance strategy,the improved evidence theory and the multi-attribute ideal reality c...To assess the operational safety risk of long-term evolution for the metro(LTE-M)communication system more accurately,the guide maintenance strategy,the improved evidence theory and the multi-attribute ideal reality comparative analysis(MAIRCA)approaches are proposed.According to the features of LTE-M system,the risk evaluation system is established.The enhanced structural entropy weight method is used to obtain the weight.Furthermore,it is combined with nine-element fuzzy mathematics to transform the degree of membership,modifying the conflict and fusion rules to solve the confidence degree clashed problem of evidence theory.Then,the system risk grade assessment result is obtained.For the purpose of forming the ranking of indicator importance,the MAIRCA is introduced and the ranking is three-dimensional.The operational state of the metro line is used as the data source in various ways the obtained risk grade increased by 7.12%.It is verified that MAIRCA can be applied to the field of urban rail transit because it has based on the test and calculation.The results show that the method is effective;compared with others,the confidence degree of excellent stability and the ranking result of risk factors is reasonable.The influencing indicator with the highest importance is the'equipment failure rate".展开更多
文摘Cardiovascular Diseases (CVDs) pose a significant global health challenge, necessitating accurate risk prediction for effective preventive measures. This comprehensive comparative study explores the performance of traditional Machine Learning (ML) and Deep Learning (DL) models in predicting CVD risk, utilizing a meticulously curated dataset derived from health records. Rigorous preprocessing, including normalization and outlier removal, enhances model robustness. Diverse ML models (Logistic Regression, Random Forest, Support Vector Machine, K-Nearest Neighbor, Decision Tree, and Gradient Boosting) are compared with a Long Short-Term Memory (LSTM) neural network for DL. Evaluation metrics include accuracy, ROC AUC, computation time, and memory usage. Results identify the Gradient Boosting Classifier and LSTM as top performers, demonstrating high accuracy and ROC AUC scores. Comparative analyses highlight model strengths and limitations, contributing valuable insights for optimizing predictive strategies. This study advances predictive analytics for cardiovascular health, with implications for personalized medicine. The findings underscore the versatility of intelligent systems in addressing health challenges, emphasizing the broader applications of ML and DL in disease identification beyond cardiovascular health.
基金Projects(50978088,51039001,51178172,51009063) supported by the National Natural Science Foundation of ChinaProject(NCET-08- 180) supported by the Program for New Century Excellent Talents in University from the Ministry of Education of China
文摘Assisted by framework of multimedia total exposure model for hazard waste sites(CalTOX),potential influences of scenario-uncertainty on multimedia health risk assessment(MHRA) and decision-making were quantitatively analyzed in a primary extent under the Chinese scenario case by deliberately varying the two key scenario-elements,namely conceptual exposure pathways combination and aim receptor cohorts choice.Results show that the independent change of one exposure pathway or receptor cohort could lead variation of MHRA results in the range of 3.6×10-6-1.4×10-5 or 6.7×10-6-2.3×10-5.And randomly simultaneous change of those two elements could lead variation of MHRA results at the range of 7.7×10-8-2.3×10-5.On the basis of the corresponding sensitivity analysis,pathways which made a valid contribution to the final modeling risk value occupied only 16.7% of all considered pathways.Afterwards,comparative analysis between influence of parameter-uncertainty and influence of scenario-uncertainty was made.In consideration of interrelationship among all types of uncertainties and financial reasonability during MHRA procedures,the integrated method how to optimize the entire procedures of MHRA was presented innovatively based on sensitivity analysis,scenario-discussion and nest Monte Carlo simulation or fuzzy mathematics.
基金supported by grants from the National Natural Science Foundation of China(Grant No.61661027)the Gansu Provincial Department of Education:Excellent Postgraduate‘Innovation Star’Project(Grant No.2022CXZX-619).
文摘To assess the operational safety risk of long-term evolution for the metro(LTE-M)communication system more accurately,the guide maintenance strategy,the improved evidence theory and the multi-attribute ideal reality comparative analysis(MAIRCA)approaches are proposed.According to the features of LTE-M system,the risk evaluation system is established.The enhanced structural entropy weight method is used to obtain the weight.Furthermore,it is combined with nine-element fuzzy mathematics to transform the degree of membership,modifying the conflict and fusion rules to solve the confidence degree clashed problem of evidence theory.Then,the system risk grade assessment result is obtained.For the purpose of forming the ranking of indicator importance,the MAIRCA is introduced and the ranking is three-dimensional.The operational state of the metro line is used as the data source in various ways the obtained risk grade increased by 7.12%.It is verified that MAIRCA can be applied to the field of urban rail transit because it has based on the test and calculation.The results show that the method is effective;compared with others,the confidence degree of excellent stability and the ranking result of risk factors is reasonable.The influencing indicator with the highest importance is the'equipment failure rate".
