###### Abstract
On the other hand, to allow a practicing as an assistant tool for medical doctors, solid knowledge and skills are strongly required [13]. This is because any misinterpretation of the vital signs increases the diagnostic error, which is estimated to be 0\(\sim\)15% wrong in different specialties such as general practice, emergency, and internal medicine [13]. Machine learning (ML) techniques have been widely implemented for data mining of medical applications since they have proven to be excellent tools for analyzing big data and revealing hidden patterns that can assist in making precise medical decisions [14; 15]. Data mining in the medical field deals with patient outcomes, thereby applying machine learning techniques necessitates precise and careful decisions to achieve a high level of accuracy [16]. AI algorithms can accurately interpret and extract patterns from the vital signs and medical examinations of cardiac patients, particularly in identifying critical symptoms. These techniques enable the detection and precise identification of anomalies, facilitating early signs detection of cardiovascular diseases and timely intervention. This capability can significantly improve cardiac care and potentially save lives [17]. Moreover, identifying risk factors for heart-related issues is essential for effective patient risk stratification, which can direct management and preventive measures to decrease related morbidity and healthcare costs [18; 19]. Due to the numerous and diverse predictors affecting health-related factors, machine learning has emerged and proved to be a leading technique [19]. Consequently, the accumulation of healthcare for personalized treatment can be facilitated through employing machine learning approaches to help analyze patients' symptoms and predict whether or not a patient has developed risk factors that require urgent treatment. In this regard, only patients exhibiting severe symptoms will be transferred to the emergency room or ICU [20; 21]. In response to these challenges, this paper presents a novel machine learning-based model that can be used to precisely anticipate the status of cardiac patients. The model employs a multi-class classification technique that encodes the most crucial risk factors and vital signs to reduce the computation complexity while increasing the prediction accuracy, achieving 97.3% and 100% minimum and maximum accuracy, respectively, based on K-fold cross-validation analysis. We have developed a real-time tracking system designed to monitor the health status of cardiac patients remotely, enabling comprehensive performance assessments that enhance the stability and efficiency of the healthcare system for cardiac patients. Below, we itemize the contributions of this manuscript as follows. We curated a balanced dataset of 750 patient records from three hospitals, categorized into three health statuses, namely stable, unstable non-critical, and unstable critical. This dataset serves as a valuable resource for training and evaluating machine learning models. 2. Development of an efficient ANN classifier designed to achieve competitive accuracy compared to state-of-the-art ML models while optimizing the architecture by reducing the number of hidden-layer neurons. 3. Introducing a novel encoding-based ML algorithm proposed that surpasses conventional methods, such as Naive Bayes, SVM, and KNN, in classification accuracy for predicting cardiac patient health, achieving up to 98.8% accuracy. 4. The proposed encoding-based algorithm has demonstrated the efficacy of the encoding-based ML approach in enabling accurate predictions, supporting proactive monitoring and timely medical interventions. 5. Implementing a LabVIEW-based monitoring system by integrating the proposed framework into a LabVIEW environment with an intuitive GUI, facilitating real-time remote monitoring for cardiac patients. This system enhances medical decision-making by allowing medical doctors to track patient status and provide immediate essential consultations and therapy.