An explainable artificial intelligence and Internet of Things framework for monitoring and predicting cardiovascular disease

Cardiovascular disease (CVD) is a leading cause of death globally. The unpredictability and severity of CVDs, such as sudden cardiac arrests, necessitate real-time monitoring and prediction using the Internet of Things (IoT) and artificial intelligence (AI) for timely intervention. Existing AI models and IoT frameworks for CVD prediction often lack integration of diverse data and fail to provide transparency in predictions, reducing user confidence and treatment effectiveness. We propose an explainable-IoT framework leveraging eXplainable AI (XAI) in the cloud layer and a mobile application to swiftly communicate predictions and explainability to patients, healthcare providers, and other users, facilitating proactive management and informed decisions. The framework integrates sensor data with cloud-based medical records to improve cardiovascular care and build user trust. Using support vector machine (SVM), random forest (RF), k-nearest neighbor (KNN), and deep neural network (DNN), we develop and evaluate CVD prediction models on two datasets: a heart disease dataset (D1) and the Cleveland dataset (D3). Additionally, we use a synthetic dataset (D2) for comparative analysis. The models are evaluated based on accuracy, precision, recall, area under the curve, time, and F1 score. For D3, SVM achieved the best accuracy (84.62%), while RF performed best on D1 and D2 (92.44% and 98.06%, respectively), comparable to state-of-the-art works. Our results highlight the importance of underrepresented physiological features in CVD datasets and the need for comprehensive datasets to enhance CVD model development. Furthermore, while synthetic data (D2) is effective for initial modeling, it requires validation with real-world data for reliable CVD prediction.