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
森林是碳库,具有强大的固碳增汇功能,在应对气候变化中发挥着重要作用。然而,由于极端高温的影响,频繁发生可燃物自燃而引发森林火灾,除了影响区域水文大气循环过程以外,也给人类带来严重的人员伤亡和经济损失。现有森林火灾预测研究主...森林是碳库,具有强大的固碳增汇功能,在应对气候变化中发挥着重要作用。然而,由于极端高温的影响,频繁发生可燃物自燃而引发森林火灾,除了影响区域水文大气循环过程以外,也给人类带来严重的人员伤亡和经济损失。现有森林火灾预测研究主要侧重可燃物研究和火灾监测等方面,较少关注大尺度地形、气象和人类活动对森林火灾的影响,但这些也是除可燃物外导致森林火灾发生的主要因素。以嘉陵江流域重庆段为研究区,区域内山地受自然火灾影响严峻。基于地理信息系统叠加地理空间因子与火灾分布点获得数据集,构建4种机器学习模型,测试模型性能,评价最优模型进行森林火灾灾害风险制图。研究结果表明,模型评估指标受试者工作曲线下面积(area under the curve,AUC)平均值为95.0%,模型性能梯度提升决策树最优,AUC值为98.3%。利用梯度提升决策树(gradient boosting decision tree,GBDT)模型预测森林火灾风险对防范大尺度森林火灾具有一定的可行性,对山城避灾规划起到借鉴作用,规划引导降低森林火灾风险,从而维护生态平衡和生态系统碳汇能力。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.
文摘森林是碳库,具有强大的固碳增汇功能,在应对气候变化中发挥着重要作用。然而,由于极端高温的影响,频繁发生可燃物自燃而引发森林火灾,除了影响区域水文大气循环过程以外,也给人类带来严重的人员伤亡和经济损失。现有森林火灾预测研究主要侧重可燃物研究和火灾监测等方面,较少关注大尺度地形、气象和人类活动对森林火灾的影响,但这些也是除可燃物外导致森林火灾发生的主要因素。以嘉陵江流域重庆段为研究区,区域内山地受自然火灾影响严峻。基于地理信息系统叠加地理空间因子与火灾分布点获得数据集,构建4种机器学习模型,测试模型性能,评价最优模型进行森林火灾灾害风险制图。研究结果表明,模型评估指标受试者工作曲线下面积(area under the curve,AUC)平均值为95.0%,模型性能梯度提升决策树最优,AUC值为98.3%。利用梯度提升决策树(gradient boosting decision tree,GBDT)模型预测森林火灾风险对防范大尺度森林火灾具有一定的可行性,对山城避灾规划起到借鉴作用,规划引导降低森林火灾风险,从而维护生态平衡和生态系统碳汇能力。
