用于早期心血管疾病风险评估的机器学习与深度神经网络混合预测集成模型
A Hybrid Predictive Ensemble of Machine Learning and Deep Neural Networks for Early Cardiovascular Disease Risk Assessment
AI总结：
本研究提出一种集成机器学习与深度神经网络的智能框架，利用IoMT设备数据经预处理后，通过SVM、Random Forests、XGBoost集成模型评估心血管疾病风险，实验证实其早期评估效率与临床决策支持价值。
AI中文摘要：
本研究提出了一种智能框架，该框架集成机器学习与深度神经网络集成技术，用于心血管疾病的早期检测与预后。该系统利用从医疗物联网（IoMT）设备收集的实时生理数据，包括心电图（ECG）传感器、心率监测仪和血压追踪器。为确保输入数据的准确性与可靠性，采用降噪、归一化及缺失值插补等预处理步骤。通过高效特征选择方法确定最关键的健康指标，再经优化后的分类器处理，这些分类器包括支持向量机（SVM）、随机森林（Random Forests）与极端梯度提升（XGBoost），它们被组合成集成架构以提升诊断精度。该框架在预测心血管疾病风险方面表现出色，与传统方法相比，实现了更高的准确率、更低的假阳性率及更强的一致性。其构建于基于云的基础设施之上，确保可扩展性与实时处理能力，用于患者的连续监测。在真实世界心血管数据集上的实验评估证实，该框架在早期风险评估与临床决策支持方面的效率。结果表明，结合传统机器学习与深度学习范式，有望实现主动式医疗管理并改善患者预后。
英文摘要：
This study introduces an intelligent framework that integrates machine learning and deep neural network ensemble techniques for early detection and prognosis of cardiovascular diseases. The system utilizes real-time physiological data collected from Internet of Medical Things (IoMT) devices, including ECG sensors, heart rate monitors, and blood pressure trackers. To ensure the accuracy and reliability of input data, preprocessing steps such as noise reduction, normalization, and missing value imputation are employed. The most significant health indicators are identified through effective feature selection methods and then processed using optimized classifiers such as Support Vector Machines (SVM), Random Forests, and eXtreme Gradient Boosting (XGBoost), which are combined in an ensemble architecture to improve diagnostic precision. The framework demonstrates remarkable performance in predicting cardiovascular disease risk, achieving higher accuracy, reduced false positives, and enhanced consistency compared to conventional methods. It is designed on a cloud-based infrastructure that ensures scalability and real-time processing for continuous patient monitoring. Experimental evaluation on real-world cardiovascular datasets confirms the framework's efficiency in early-stage risk assessment and clinical decision support. The results highlight the potential of combining traditional machine learning and deep learning paradigms to achieve proactive healthcare management and improve patient outcomes.
