# Artificial Intelligence, Sensors and Vital Health Signs: A Review
## 5 Artificial Intelligence (AI) for Healthcare Delivery
### Intelligent Algorithms for Vital Signs Monitoring  
Different machine learning and data analysis methods proposed in many studies can be applied to physiological signs. For instance, linear support vector machines (SVM) reported in [86] were successfully applied in the analysis and classification of physiological parameters such as BP, HR, SPO2, glucose level, etc. Supervised ML classifiers such as decision trees (DT), Naive Bayes (NB), linear regression (LR), random forest (RF), support vector machine (SVM), and k nearest neighbour (KNN) were the most used approaches for disease detection, recognition, and classification [87]. Moreover, classes of artificial neural networks (ANN) classified has also been used in health-related areas. The earliest studies in the field of ANN include long short-term memory (LTSM) and recurrent neural networks (RNN), as reported in [88]. Table 4 presents a summary of the AI methods used in vital signs diagnosis. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Citation** & **AI/ML Methods** & **Application** & **Description** & **Function** \\ \hline [PERSON] & \"RF/SVM\" & “Heart disease diagnosis” & “Creation of classification and regression analysis” & “Develop a hyperplane. Use in pattern analysis puzzles and nonlinear regression” \\ \hline [PERSON] et al. [90], 2018 & “NB” & “Heart disease diagnosis” & “Probabilistic classifiers” & “Creation of classification, sentiment analysis, spam filtering, and news classification” \\ \hline [PERSON] et al. [91], 2019 & “Cluster analysis and efficient differentially private data clustering scheme” & “Heart disease diagnosis” & “Classify a sample of subjects (or objects) based on a set of measured variables in different groups” & “Interaction using the K-means algorithm” \\ \hline [PERSON] et al. [92], 2019 & “Convolutional neural network (CNN)” & “Heart disease diagnosis” & “Classify patients with known as shift invariant or space invariant artificial neural networks (SIANN)” & “Classify patients with ventricular dysfunction” \\ \hline [PERSON] et. al. [93], 2019 & “DL” & “Heart disease diagnosis” & “DNN learning and a prediction mode” & “Enable machines to process data with a nonlinear approach” \\ \hline [PERSON] et al. [94], 2018 & “Recurrent fuzzy neural network” & “Heart disease diagnosis” & “Neural Classifier” & “Using DPSS to help prevention healthcare services and data security” \\ \hline [PERSON] et al. [95], 2019 & “Grasshopper optimization” & “Heart disease diagnosis” & “Gravity force and the wind advection AI” & “Multi-linear perceptron (MLP) ANN for tackling optimization with flexible and adaptive searching methodology” \\ \hline [PERSON] et. al. [96], 2019 & “Reliefff and RS” & “Heart disease diagnosis” & “Approach as an integrated feature selection system for heart disease diagnosis” & “To data analysis and data mining that has been applied successfully to many real-life problems in medicine, pharmacology” \\ \hline \hline \end{tabular}
\end{table} 
Table 4: AI/ML methodology for IoT and vital signs diagnosis. Recently, an automated computerized tomography (CT) quantification of epicardial adipose tissue using the deep neural network (DNN) approach was presented in [106]. In this study, a fully automated deep convolutional neural network (CNN) method was trained to predict EAT on non-contrast material-enhanced calcium-scoring CT scans from multiple cohorts, scanners, and protocols. According to [89], RF outperformed the state-of-the-art ML algorithm used on clinical data. Another closely related review on ML with WSN in smart healthcare was presented in [98]; the study presented different categories of AI-powered WSNs and IoT used in the healthcare domain. [107] presented a comprehensive review of the ML techniques used in healthcare systems. Furthermore, a class of RNNs known as gated recurrent units was used to develop a DL model, as reported in [99]. The authors' novel contribution has led to broader research dimensions in the area of clinical data analysis.