# Toward QoS Monitoring in IoT Edge Devices Driven Healthcare--A Systematic Literature Review
## 4 Preliminary Concepts
### Machine Learning  
Artificial intelligence, a new assistant for doctors, can help them with diagnostics and even prognoses. The ability to learn from experience can also make it easier to fully understand the conditions of the patients. The use of AI algorithms in SHAs allows for the creation of computer programs that acquire knowledge and grow as a result of experience rather than being explicitly programmed to make predictions or suggestions. Over the past few decades, technological advancements in computer power have made it possible for resource-intensive AI methods, like machine learning solutions, to be created. Machine learning programs use information for training, or data samples, to statistically develop a predictive model, as shown in Figure 11[29]. Applications that are needed for sound decision-making employ this training to categorize things or make predictions. Smart healthcare applications use machine learning logic to process health data gathered from sensors and send it to the cloud for processing using machine learning algorithms. The received data are referred to as the testing data, and the outcomes are adequately emulated. After being emulated, the results will also be used for the training portion of the forthcoming testing data. The data obtained by the sensors are therefore viewed as testing data and, following processing, as training data for further medical assessments. Computational efficiency, detection precision, and robust implementation are the most important factors to consider when choosing machine learning algorithms for healthcare applications [30]. Figure 10: Venn diagrams show the key similarities and technical differences between the two computing paradigms. Typically, supervised learning, unsupervised learning, and reinforcement learning are used to categorize machine learning systems. The use of labeled datasets to train algorithms that accurately categorize or predict data or outcomes is the definition of supervised learning. By giving the algorithm examples of inputs and the anticipated outputs that they should produce, the goal is for the program to learn a general rule that connects inputs to outputs. Unsupervised learning refers to the algorithms used to find patterns in data sets, including data points that are neither categorized nor labeled. The learning algorithm does not receive labels; instead, it independently uncovers hidden patterns in the data and works as a feature extraction tool. An algorithm for reinforcement learning can perceive and comprehend its environment, act and learn from mistakes, and decide what actions intelligent agents should take to maximize the concept of cumulative reward in a particular environment. As it traverses its issue area, the learning algorithm interacts with a dynamic environment and receives feedback that could be compared to rewards, which it seeks to maximize [31]. A subset of machine learning known as \"deep learning\" algorithms may learn to carry out categorization tasks directly from images, text, or voice. They are supported by synthetic neural networks. Vital sign data is gathered via sensors, which enables AI to spot trends in real-time. Triage systems powered by AI select cases according to their urgency, speeding up emergency response. By using the aforementioned AI techniques, large amounts of data produced by health sensors may be processed quickly and easily. This swift processing aids in prompt diagnosis, which decreases latency. Additionally, the use of AI-enabled remote monitoring devices, automation of the patient's screening process, instant notifications, and predictive algorithms aid doctors in making timely decisions and substantially improve response time. Deep learning models are developed using a large amount of sample data and neural network designs with numerous layers to reach state-of-the-art accuracy [32].