## 4 Artificial Intelligence (AI) in Healthcare  
AI is fast becoming a catchword globally as it aims to simplify human work and make it more efficient. Thus, AI is playing a critical role in strengthening and transforming industries including the healthcare sector [79]. Moreover, in the healthcare sector, AI technology plays a significant role in minimizing human errors and complementing conventional healthcare processes. The application of AI techniques in healthcare can be done in several ways such as advanced patient diagnostics, disease prevention, medical therapy and informed clinical decision-making [80]. Based on this premise, the application of AI techniques in healthcare delivery has remarkably increased. This section reviews the use of ML, a subfield of AI, in clinical analytics using wearable sensor data. Different types of wearable sensors are available to collect clinical data. Thus, these datasets can often be processed using appropriate ML techniques. Figure 7 presents an ML-based healthcare system modelled with data from wearable sensors. Wearable sensors enable the monitoring of vital health parameters, which can be used to detect many illnesses such as chronic clinical disorders, diabetes, hypertension, and some others. A patient's vital health signs such as blood glucose level, blood pressure, body temperature, oxygen saturation and pulse rate are measured using a variety of clinical-related sensors. Then, Internet of Things (IoT)-based platforms such as \"Think speak\" are utilized to send the data values measured from these sensors to the cloud. Think speak is an open IoT platform enabled by Arduino or Arduino-compatible hardware for data visualization. It allows writing or reading data to be put into or taken from the platform. This supports the data storage from the API or sensors in the cloud for either a private or public channel. ML techniques are then used to analyze the stored data in the channel. The diagnosis of the symptoms to identify the disease(s) affecting the patient can be performed using the data collected with the sensor. ML algorithms are hence used to classify and identify the disease. Undoubtedly, the classification capacity of ML algorithms is known to be effective, and the reason for their broad use in the health industry is their ability to classify and predict diseases based on their symptoms (health-related data). Currently, many technologies are using ML algorithms for data analytics to obtain useful insights [81; 82; 83; 84]. Supervised and unsupervised learning algorithms are the two broad classifications of ML algorithms. When the prediction of the values for the training dataset is the goal, supervised learning algorithms are used, while unsupervised learning algorithms are used for the identification of a specific label from the clustered labels [85; 86; 87]. Classification and regression are the most commonly used supervised learning algorithms [88]. Some examples of classification algorithms include Naive Bayes (NB), k-Nearest Neighbor (kNN), Artificial Neural Network (ANN), Decision Tree (DT) and  
Figure 7: Machine Learning-based model via wearable sensors data for healthcare systems. Support Vector Machines (SVM) [89; 90; 91; 92]. Discriminant Analysis (DA), Logistic Regression (Log) and Linear Regression (LR) are prominent regression algorithms. C-Means, K-Means, BIRCH, DBSCAN and X-Means algorithms are examples of clustering techniques which are classified as unsupervised learning algorithms [93; 94; 95; 96]. Figure 8 provides a detailed classification of ML techniques. In practice, all these ML algorithms can be employed to address problems concerning medical image recognition, prediction and detection of diseases, behavioural adjustments and so on. The recent technological advancement that brought about wearable sensors has prompted several researchers to explore their potential in the concept of integrating AI with wearable sensors in the healthcare domain.