article · Bioengineering
Cardiovascular diseases remain a leading cause of death globally, yet conventional risk assessment approaches such as the Framingham Risk Score, clinical assessments, and static tests struggle with limited precision and an inability to adapt to new patient data. To address these issues, CardioRiskNet was developed as a hybrid artificial intelligence model for cardiovascular disease risk prediction and prognosis. The system integrates data preprocessing, feature selection, active learning, attention mechanisms, and explainable artificial intelligence across seven functional components. The active learning component iteratively identifies informative samples, whilst attention mechanisms focus on key clinical features. In experimental tests, the model achieved 98.7 percent accuracy, 98.7 percent sensitivity, 99.0 percent specificity, and a 98.7 percent F1-score. By incorporating explainable artificial intelligence, the platform also provides transparent decision-making to assist clinical assessments.
Cardiovascular disease assessment often relies on static risk scores that cannot easily incorporate dynamic patient information. Developing high-accuracy machine learning tools that explain their reasoning helps clinicians understand the basis of predictions. Transparent, automated risk assessment could help healthcare providers detect potential cardiovascular complications earlier and tailor care to individual patient profiles.
The primary application is a clinical decision-support tool for healthcare professionals managing cardiovascular disease risk. Because the architecture includes deployment and integration components alongside validated experimental results, the technology appears to be applied and tested in an experimental setting. Moving towards routine clinical adoption will depend on integrating the software into existing hospital systems and validating performance across broader clinical workflows.
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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.
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DOI: 10.3390/bioengineering11080822
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