## 2 Literature  
To deliver more accurate evaluations of heart illness, researchers [18] developed an IoT platform that employs a modified self-adaptive Bayesian algorithm (MSABA). The patient's  
Figure 2: Historical perspective on healthcare 1.0 to 5.0.  
wristwatch and heart rate monitor gadget captures and transmits data from the patient's pulse rate, such as their heartbeat and hypertension, to a server. In past decades, heart illness has overtaken many other causes of mortality. It is challenging to predict a person's probability of acquiring heart problems because this requires both specialist knowledge and real-world experience. An adaptive electrocardiogram (ECG) noise reduction method based on empirical mode decomposition was proposed [19]. The advantages of the suggested techniques were used to reduce the distortion-free noise in ECG readings. A common tool for assessing heart problems is ECG. A free-interference ECG is frequently desired for the accurate evaluation of heart diseases. The ability of robots to discern mental expressions in facial movements should be developed and deployed for the human involvement, information animations, and interaction between individuals and robotics to be effective. As a consequence, in current history, significant efforts have been made to address the issue of facial expression recognition (FER) using convolutional neural networks (CNNs) [20]. The privacy and dependability of drone deliveries across multiple untrusted channels may be managed by the technique known as blockchain. We suggested GaRuDa, a smart contracts drone delivery system for healthcare 5.0 uses, as a result of the preceding circumstances. Yet, owing to restricted zones, rugged terrain, war zones, poor road situations, congestion, and distant areas, the leading healthcare network infrastructure and delivery procedures among different providers struggle [21]. Researchers [22] established the idea of context-aware attributed acquisition with cipher policy-attribute-based encryption (CP-ABE) to safeguard customer data security in an IoT-enabled society in healthcare 5.0. A paradigm change in healthcare 5.0 is anticipated in the medical field, which makes use of patient-centered digital well-being and pushes the operational limits of healthcare 4.0. Through the use of aided techniques, including machine learning, the IoT, big data, and supported communication routes, healthcare 5.0 concentrates on the live monitoring of patients, contextual regulation and wellbeing, and security adherence. Healthcare 5.0 may therefore be hampered by management efficiency in the medical industry, the state testing of estimation techniques, robustness, and an absence of societal and legal structures [23]. According to a study [24], a novel methodology for automating the diagnosis of skin cancer conducts segmentation and categorization of skin lesions. The suggested scheme comprises two phases: the first stage uses a fully convolutional encoder-decoder network (FCN) to acquire the complicated and nonuniform characteristics of skin lesions, with the converter phase learning the lesion's rough esthetic and the decoder phase learning the lesion's boundaries description. By distributing a worldwide learning method via distributed cumulative servers, federated learning (FL) addresses the issues mentioned above. Innovative and proactive universal healthcare represents one of the digital technologies features made possible by the IoMT. Due to the massive size and widespread deployment of IoMT systems, privacy and protection are top issues with the IoMT [25]. Researchers [26] proposed a randomized, proactive, DL-based approach that offers end-to-end protection from network threats in IoMT devices that automatically identifies and rejects harmful data. As more IoMT gadgets and apps are used, smart healthcare faces constantly growing system vulnerabilities. The IoMT is a popular method for developing sustainable urban solutions that support vital facilities over the long term, such as smart healthcare services [27].