## 2 Related Works  
Applying machine learning concepts has been carried out in several studies [8; 22; 23; 24; 25], dealing with a variety of challenges that the healthcare industry encounters. These scholarly studies aim to support the medical field by thoroughly analyzing the medical information of a patient and providing excellent decisions that help improve healthcare systems to potentially open new industrial paths for different applications. Data analytics in medical diagnostics has been substantially employed to develop risk prediction models that involve machine learning approaches for decision support systems, intervention, and resuscitation [26; 27] to lower healthcare costs. The authors in [14] carried out a comparison between implementing a multilayer backpropagation perceptron network (MLPN) and Support Vector Machine (SVM) algorithms to diagnose five classes of heart disease with a dataset of 303 patients. The results revealed that applying SVM for online classification achieves higher prediction accuracy by delivering more accurate decisions. An economic model that also implements the SVM algorithm was proposed in [28]. It was simulated as an alternative to DNA testing to identify possible threats that form the foundation of diagnostic tests and disease prevention. This indicates that the presented model is able to predict unhealthy lifestyles arising from diseases that patients might suffer from. The developed model can provide a detailed report that tracks changes in an individual's diet and weight to help maintain a healthy lifestyle. In [29; 30], the authors applied machine learning techniques to conduct a survey that predicts heart diseases at the early stages. They employed various ML-based algorithms such as SVM, Naive Bayes, Decision Tree, K-Nearest Neighbor (KNN), and ANN to aware cardiac patients in case they develop complications. The survey concludes that higher performance and classification accuracy can be realized when the input features are selected efficiently by applying search methods first to specify only the most suitable features. Likewise, ML-based concepts have also been employed in [31] to improve the healthcare system through patient satisfaction by determining related characteristics of various parameters. The model concentrates on specific criteria to build a safe and supportive healthcare environment. However, the decision-making process is not adequately matured to provide a precise decision (88% regression accuracy). Predicting hospitalization emergencies for cardiac patients was studied in [8; 32]. The model developed in [8] takes several risk factors, such as age, disease types, disease conditions, etc., as input features and analyzes them to identify those associated with the high risk, whereas the authors in [32] employed data mining techniques to specify patients at high risk so that they could accurately and efficiently manage the admission of inpatients at the emergency department. Even though the predictive model facilitates emergency admissions and patient satisfaction, it only achieves a maximum classification accuracy of 80.31%. Therefore, clinical examination remains necessary. Additionally, IoT-based patient monitoring systems have been widely researched in the literature. The researchers in [22] proposed an IoT-based healthcare system that leverages remote monitoring. This would help in advancing the healthcare business by reducing consultations and in-person meetings with doctors. However, integrating the classification model into the monitoring system requires a real-time GUI-based display to indicate potential risks at early stages and then provide timely treatment and life care. In [33], the authors built an IoT-based architecture that can be employed in various types of online systems. Nevertheless, deep investigations are still in demand to achieve more customized models that provide an acceptable level of security and prediction outcomes.