心血管疾病(CVDs)的全球流行是导致死亡的主要原因,这凸显了对完善的风险评估和预测方法的迫切需要。传统的方法,包括弗雷明汉风险评分,验血,成像技术,和临床评估,虽然被广泛使用,受到缺乏精确度等限制的阻碍,对静态风险变量的依赖,以及无法适应新的患者数据,因此,有必要探索替代策略。作为回应,这项研究引入了CardioRiskNet,旨在超越这些限制的基于混合AI的模型。拟议的CardioRiskNet包括七个部分:数据预处理,特征选择和编码,eXplainableAI(XAI)集成,主动学习,注意机制,风险预测和预后,评估和验证,以及部署和集成。起初,通过清理数据对患者数据进行预处理,处理缺失的值,应用规范化过程,并提取特征。接下来,选择信息量最大的特征,并将分类变量转换为数值形式。特别是,CardioRiskNet采用主动学习来迭代地选择信息丰富的样本,提高学习效能,而其注意力机制动态关注相关特征,以进行精确的风险预测。此外,XAI的整合促进了决策过程的可解释性和透明度。根据实验结果,CardioRiskNet在准确性方面表现出卓越的性能,灵敏度,特异性,和F1-Score,值为98.7%,98.7%,99%,98.7%,分别。这些研究结果表明,CardioRiskNet可以准确评估和预测CVD风险,展示了主动学习和人工智能超越传统方法的力量。因此,CardioRiskNet的新颖方法和高性能推进了心血管疾病的管理,并为医疗保健专业人员提供了患者护理的强大工具。
The global prevalence of cardiovascular diseases (CVDs) as a leading cause of death highlights the imperative need for refined risk assessment and prognostication methods. The traditional approaches, including the Framingham Risk Score, blood tests, imaging techniques, and clinical assessments, although widely utilized, are hindered by limitations such as a lack of precision, the reliance on static risk variables, and the inability to adapt to new patient data, thereby necessitating the exploration of alternative strategies. In response, this study introduces CardioRiskNet, a hybrid AI-based model designed to transcend these limitations. The proposed CardioRiskNet consists of seven parts: data preprocessing, feature selection and encoding, eXplainable AI (XAI) integration, active learning, attention mechanisms, risk prediction and prognosis, evaluation and validation, and deployment and integration. At first, the patient data are preprocessed by cleaning the data, handling the missing values, applying a normalization process, and extracting the features. Next, the most informative features are selected and the categorical variables are converted into a numerical form. Distinctively, CardioRiskNet employs active learning to iteratively select informative samples, enhancing its learning efficacy, while its attention mechanism dynamically focuses on the relevant features for precise risk prediction. Additionally, the integration of XAI facilitates interpretability and transparency in the decision-making processes. According to the experimental results, CardioRiskNet demonstrates superior performance in terms of accuracy, sensitivity, specificity, and F1-Score, with values of 98.7%, 98.7%, 99%, and 98.7%, respectively. These findings show that CardioRiskNet can accurately assess and prognosticate the CVD risk, demonstrating the power of active learning and AI to surpass the conventional methods. Thus, CardioRiskNet\'s novel approach and high performance advance the management of CVDs and provide healthcare professionals a powerful tool for patient care.