# Artificial Intelligence, Sensors and Vital Health Signs: A Review
## 5 Artificial Intelligence (AI) for Healthcare Delivery
### Intelligent Algorithms for Vital Signs Monitoring
Recently, another class of supervised DNN, called CNN in [98, 100], has been used in medical data analysis and classifications. The CNN is a class of the ANN that is made up of thousands of neurons and hidden layers. It is mostly applied to visual imagery (medical images and vital signs). It is considered end-to-end automated learning as it does not involve human intervention [108, 109, 110]. Several researchers in the area of cardiovascular monitoring in the healthcare system have explored AI, in particular, ML algorithms [111, 112, 113, 114, 115]. The most common ML techniques used in heart disease diagnosis are SVM and NB. According to [101], SVM and NB ML methods are used in heart disease diagnosis systems. The SVM develops a set of \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Citation** & **AI/ML Methods** & **Application** & **Description** & **Function** \\ \hline [PERSON] et al. [95], 2019 & “Multilayer perceptron (MLP)” & “Heart disease diagnosis” & “MLP and ANNs technology together” & “Provide various continuous functions” \\ \hline [PERSON] et al. [97], 2018 & “KNN, NB, SVM, LR, support vector regression, classification trees, regression trees, and RF” & “Prediction outpatient treatment” & “ML with IoT data for risk prediction” & “The model is a set of hypotheses about dialysis biomarkers proved in a probabilistic format” \\ \hline [PERSON] et al. [98], 2021 & “Supervised Learning and Reinforcement Learning” & “Prediction outpatient treatment” & “Classifications and Prediction models” & “Provides different procedures used in all the three learning styles” \\ \hline [PERSON] et al. [99], 2019 & “CNN” & “Prediction outpatient treatment” & “Deep learning model for predicting chemical composition” & “Developed CNN, like stacked auto-encoders, deep belief networks, and RNN” \\ \hline [PERSON] et al. [100], 2019 & “TORS” & “Robot surgery” & “Transoral Robotic Surgery” & “Fewer blood losses, faster postoperative recovery, and fewer adhesions” \\ \hline [PERSON] et. al. [101], 2010 & “AESOP” & “Robot surgery” & Automatic Endoscopic System for Optimal Positioning & “Robotic endoscope and surgical robotic systems” \\ \hline [PERSON] et. al. [102], 2019 & “STAR” & “Robot surgery” & “Smart Tissue Autonomous Robot” & “Nascent clinical viability of a self-governing soft-tissue surgical robot” \\ \hline [PERSON] et. al. [103], 2018 & “5G-Smart Diabetes” & “Personalized healthcare” & “Personalized diabetes diagnosis” & “Real-time system to analysis diabetes suffering” \\ \hline [PERSON] et. al. [104], 2018 & “DeepSurv” & “Personalized healthcare” & “DNN and state-of-the-art survival method” & “Provide individual treatment recommendations” \\ \hline [PERSON] et. al. [105], 2019 & “BioSenHealth 1.0” & “Personalized healthcare” & “Real-time monitoring of vital statistics of patients” & “Live data access using thingspeak.com cloud platform” \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._hyperplanes in the infinite-dimensional area. It estimates the linear separation surface with a maximum margin in a provided training dataset. Also, SVM has been widely used in pattern analysis puzzles and nonlinear regression. However, it does not make any strong hypotheses about the data [116; 117; 118; 119]. The NB method is mostly used in text recognition, classifications, and spam filtering [120; 121; 122]. The NB performs well if the input data are arranged in distinct groups and requires less data than other ML approaches [102]. Moreover, there is a need for a mobile heart rate monitoring system for \"atrial fibrillation detection\". The mobile system achieved real-time \"arrhythmia detection\". In addition to conventional wearable healthcare devices, ML models can be applied to more efficient approaches to HR diagnosis via remote patient monitoring [92]. According to the study in [43], \"ML models can predict the possibility of an occurrence of a heart attack in the nearest future.