###### Abstract  
Remote monitoring of a patient's vital activities has become increasingly important in dealing with various medical applications. In particular, machine learning (ML) techniques have been extensively utilized to analyze electrocardiogram (ECG) signals in cardiac patients to classify heart health status. This trend is largely driven by the growing interest in computer-aided diagnosis based on ML algorithms. However, there has been inadequate investigation into the impact of risk factors on heart health, which hinders the ability to identify heart-related issues and predict the conditions of cardiac patients. In this context, developing a GUI-based classification approach can significantly facilitate online monitoring and provide real-time warnings by predicting potential complications. In this paper, a general framework structure for medical real-time monitoring systems is proposed for modeling the vital signs of cardiac patients in order to predict the patient's status. The proposed approach analyzes AI-driven interventions to provide a more accurate cardiac diagnosis and real-time monitoring system. To further demonstrate the validity of the presented approach, we employ it in a LabVIEW-based remote tracking system to predict three healthcare statuses (stable, unstable non-critical, and unstable critical). The developed monitoring system receives various information about patients' vital signs, and then it leverages a novel encoding-based machine learning algorithm to pre-process, analyze, and classify patient status. The developed ANN classifier and proposed encoding-based ML model are compared to other conventional ML-based models, such as Naive Bayes, SVM, and KNN for model accuracy evaluation. The obtained outcomes demonstrate the efficacy of the presented ANN and encoding-based ML approaches by achieving an accuracy of 98.4% and 98.8% for the developed ANN classifier and the proposed encoding-based technique, respectively, whereas Naive Bayes and quadratic SVM algorithms realize 94.8% and 96%, respectively. In short, this study aims to explore how ML algorithms can enhance diagnostic accuracy, improve real-time monitoring, and optimize treatment outcomes. Meanwhile, the proposed tracking system outperforms most existing monitoring systems by offering high classification accuracy of the heart health status and a user-friendly interactive interface. Therefore, it can potentially be utilized to improve the performance of remote healthcare monitoring for cardiac patients. c 2025 18 9  
Intensive Care Unit (ICU) [1; 2]. Fortunately, due to advances in healthcare technologies, the lifespan of chronic patients has been effectively extended [3]. People suffering from heart disease often require long-term daily monitoring of their vital signs, including blood pressure, heart beating rate, respiratory rate, chest pain, and other factors [4; 5]. The available cardiac monitoring systems are very expensive since they rely on many complicated hardware devices that employ complex algorithms [6]. Additionally, medical care costs have been exponentially increased due to the growing number of people suffering from chronic diseases such as diabetes and cardiac disease [7; 8; 9]. Typically, complicated measurement devices are utilized in hospitals worldwide to diagnose serious heart diseases. Meanwhile, these types of healthcare equipment require physical in-person examination by medical specialists to identify any arrhythmia issues, i.e., irregular heartbeats [10]. One strategy for overcoming the healthcare delivery challenges of chronic diseases is to utilize a remote patient management system [11; 12]. LabVIEW-based patient real-time monitoring systems have been sought to cope with unaffordable medical care. This is due to LabVIEW offering an excellent Graphical User Interface (GUI) for remote tracking that can be shared online via the LabVIEW webserver. In addition, LabVIEW provides a more user-friendly interactive interface, which allows quick understanding and cognitive medical care.