Talaat Fatma M, Elnaggar Ahmed R, Shaban Warda M, Shehata Mohamed, Elhosseini Mostafa
Faculty of Artificial Intelligence, Kafrelsheikh University, Kafrelsheikh 33516, Egypt.
Faculty of Computer Science & Engineering, New Mansoura University, Gamasa 35712, Egypt.
Bioengineering (Basel). 2024 Aug 12;11(8):822. doi: 10.3390/bioengineering11080822.
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.
心血管疾病（CVDs）作为主要死因的全球流行凸显了对精细风险评估和预后方法的迫切需求。传统方法，包括弗明汉风险评分、血液检测、成像技术和临床评估，尽管被广泛使用，但受到缺乏精确性、依赖静态风险变量以及无法适应新患者数据等限制的阻碍，因此有必要探索替代策略。作为回应，本研究引入了CardioRiskNet，这是一个基于人工智能的混合模型，旨在克服这些限制。所提出的CardioRiskNet由七个部分组成：数据预处理、特征选择与编码、可解释人工智能（XAI）集成、主动学习、注意力机制、风险预测与预后、评估与验证以及部署与集成。首先，通过清理数据、处理缺失值、应用归一化过程和提取特征对患者数据进行预处理。接下来，选择最具信息性的特征，并将分类变量转换为数值形式。独特的是，CardioRiskNet采用主动学习来迭代选择信息性样本，提高其学习效率，而其注意力机制动态聚焦于相关特征以进行精确的风险预测。此外，XAI的集成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、敏感性、特异性和F1分数方面表现优异，值分别为98.7%、98.7%、99%和98.7%。这些发现表明，CardioRiskNet可以准确评估和预测CVD风险，证明了主动学习和人工智能超越传统方法的能力。因此，CardioRiskNet的新颖方法和高性能推动了CVD的管理，并为医疗保健专业人员提供了一个强大的患者护理工具。
