As chronic diseases turn out be vast spread, diagnosis, treatment and monitoring of the diseases are essential. Recently, with the advancements in wearable sensor devices and communication protocol, healthcare system has been supported and enriched. Outlined the method of RPM (Remote patient monitoring) which applies artificial intelligence for its implementation. Data mining, decision support system, cloud computing, Internet of Things and Wearable sensors are reviewed for its challenges, advantages, and applications (El-Rashidy et al., 2021). To extract the information of patients, preprocessing methods are used in techniques of data mining. As data mining techniques are outdated because of its storage and speed issues, big data plays an important role in storing vast volumes of patient information with the aid of storage mechanisms like HDFS and HBase. Development based on the practice-based view was enabled with bigdata analytics transformation model which reveals the capabilities of big data analytics. The model offers a view of big data in healthcare setting strategically. Identification of path-to-value chains for healthcare organizations have also been provided (Ramesh and Santhi, 2020) and (Wang et al., 2018a). Remote patient monitoring system with wearable sensors that uses IoT analytical platform is designed for cardiovascular diseases has been proposed. It can be utilized to check and monitor the current and previous health history of the patient by means of IoT even though the patient is not at the hospital. This method implements compact RPM at low cost with various wearables sensors. Data analytics platform used in this method for monitoring is ThingSpeak (Han et al., 2021). This paper presents an adaptive framework for diabetic patients monitoring that makes use of machine learning methods. Smart devices, smartphones and sensors are the elements used to gather measurements from human body. The patient data collected from the intelligent system further undergoes data classification using machine learning algorithms. The sequential minimal optimization algorithm used provides higher accuracy, precision and sensitivity (Rghioui et al., 2020). An approach named iCloud Assisted Intensive Deep Learning was introduced to provide healthcare medium. This system collects the existing records of health of patients from data repository. Training and test phase of data are performed which would be operated with intensive deep learning principles. The results of the processing are kept in the cloud repositry by IoT enabled features. The integration of IoT with machine learning processes with the smart medical gadgets is used here. Medication monitoring system is devised that utilizes the IoT and deep learning methods to prevent the sensing errors of sensor devices and enhance user experiences by identifying different activities of patients. The method captures images of patients through Open Pose to verify the medication conditions. Reliable communication between medical professional and systems was done through MQTT protocol by periodic transmissions of data (Kondaka et al., 2022) and (Roh et al., 2021). To better estimate health outcomes and improve the effectiveness of smart healthcare services for monitoring health, a Hybrid deep learning techniques was presented. Risk detection was the major concern of this system and hence it increases the efficiency of the healthcare industry. Interpretation of deep learning techniques with IoT and soft sensors helps to identify the concealed patterns. Framework of E-Healthcare monitoring system associated with machine learning techniques was designed as an advanced automation system. As this work is based on IoT applications the monitoring and decision making was performed effectively in regular and periodic manner with proper diagnosis (Rajawat et al., 2021) and (Godi et al., 2020). Additionally, we are concerned for the welfare of our elderly and disabled neighbours when we leave them at home alone for an extended amount of time while going about our daily business. Therefore, it is increasingly important to integrate technology like wearables and healthcare sensors with our healthcare systems to create a more secure and convenient living environment for everyone.

The remaining portion of the manuscript is systematized as follows: Section 2 offers a detailed description on various existing techniques employed beforehand. Section 3 is the depiction of proposed methodology. Section 4 illustrates the performance analysis of proposed work and its comparison with traditional methods. Section 5 narrates the overall conclusion of workflow.

In this section various articles of data analytics of patient monitoring and deep learning networks along with Internet of Things are reviewed.

(Harb et al., 2020) this research offered a method for avoiding vulnerable attacks and protect the information of patient on multiple information servers. In comparison with wired sensor networks, remote sensor networks are more vulnerable. The main focus of this paper is to broadcast the patient information through several information servers by using crypto systems in order with a view to preserve the confidentiality of patient information. Patient data stored in sensor networks is safer than ever before.

(Li et al., 2021) provided the specifies of an AI-based system for analyzing large healthcare datasets. As the applications of ML-based methods were used for several IoT applications recently, the healthcare tasks can be processed with IoT applicable sensor devices. Further, advantages and limitations of the existing methods along with some research challenges were presented. Business intelligence that use machine learning methods have recently emerged, giving healthcare practioners and government focused organizations a tool with which to stay completely prepared. Problems like resource allocation, traffic engineering, security and routing can be sorted out by application of ML based techniques.

(Sharma et al., 2021a) provided a framework covid-affected patients may be remotedly monitored. For early covid detection, the model offered was an Internet of Things-based,remotely accessible, alert system bio-wearable sensor system. Biomedical signals like PPG,ECG,accelerometer and temperature were extracted from bio wearable sensor devices in this method. The challenges that encompass the privacy issues and security system were analyzed by the proposed ontology remote monitoring method. The results observed from the simulations shows the efficacy of the model is high with better accuracy.

(Ani et al., 2017) suggested a cerebro vascular treatment monitoring device. By this method of monitoring, future recurrence could be minimized by providing alert to the doctors. With the applications of IoT, data analytics and decision making on real time by health constraints of the patient the medical professional can diagnose the consequences. Thereby preventive treatment can be provided for the patient to safeguard. For diagnosis and treatment, classification algorithms were utilized in this work. Tree based classification-Random Forest method delivered an accuracy of 93 percent in this proposed scheme.

(Rajan Jeyaraj and Nadar, 2019) focused on framework of novel IoT application oriented physiological signal monitoring system for e-healthcare system. Deep neural network-based system was employed for accurate signal prediction and used estimation algorithm in addition. Signal measurement was accomplished by intelligent sensor and myRIO instrument was used for data acquisition. The proposed model also had designed smart monitor for consumer. The model was validated for four physiological signal accuracies with two users. As per results, the proposed automated system has obtained higher accuracy of signal prediction and proved it to be reliable.

(Banerjee et al., 2020) Using concrete examples, you just learned about the fundamentals of big data analytics, the Internet of Things(IoT), and a hybrid type of big data that incorporates machine learning techniques and certain kinds of artificial intelligence to create structured data for remote diagnostics. The development of telemedicine and its integration with Artificial Intelligence to create structured data for remote diagnostics. The development of telemedicine and its integration with artificial intelligence in healthcare delivery was also explored. Importance of Internet of Robotic things and Advancements of telerobotic surgery have been briefly described. Further, wearable devices that targets the biomedical field evolution and healthcare applications were analyzed with standard protocols of machine learning for the system of healthcare.

(Mishra et al., 2020) big data analytics as well as IoT were recently highlighted as emerging fields related to medical fields and also analyzed the system of healthcare monitoring. The model integrates IoT and big data analytics and hence patient information was stored in the cloud. The model developed for implementation could monitor the patient’s health status and the model prediction was faster. It can be implemented at the real time for monitoring health of patient. Thereby medical experts can provide appropriate treatment in advance by earlier predictions.

(Syed et al., 2019) issues in health as well as big data analytics were examined as part of expanding field of study into creating an intelligent healthcare system. The study. s overarching objective is to provide healthcare services to both sick and healthy individuals through remote monitoring using intelligent algorithms, methodologies, and instruments. The approach utilizes sensors worn on the chest, ankle, and wrist to track a person’s everyday activities. The health status of patients may be assessed by the transmission of data from sensors attached towards the internal organs to the caregiver or doctor. At the receiving end, these signals are recorded and processed using big data analytics and machine learning methods. Alzheimer’s sufferers, the elderly, and the overweight may all benefit from remote monitoring using this device. The suggested system was proved to be more accurate in simulations.

(Wang et al., 2018b) examined on the big data analytics based on historical, architectural design and functionalities of components. The capabilities of big data analytics were identified as unstructured data analytical capability, analytical capability for patterns of care, predictive capability, traceability and decision support capability. Also benefits of big data analytics were mapped in terms of operational, organizational, IT infrastructure, strategic and managerial areas. The adoption and support of big data analytics in healthcare organizations were also discussed.

(Pandey and Prabha, 2020) provided the strategy of predicting potential outcomes in the ailment of cardiovascular patients. This analyzes and predicts the malady of cardiovascular diseases with the aid of watch pulse sensors. The reading of sensor was perused and datasets are grouped with therapeutic parameters. By the classification and calculations of artificial intelligence the work is initiated. From the dataset, the extracted information was handled with machine learning techniques. Specifically, Random backwoods classifier algorithm and Decision Tree algorithm were utilized in this work. For detecting heart diseases, Support vector machine was observed as it provides higher accurate outcome with exactness. Hence a framework of foreseeing prior heart disease was demonstrated for earlier prediction with mass screening system.

(Sarmah, 2020) presented a method for monitoring cardiac patients that makes use of Deep Learning modified neural network to aid in the detection and treatment of illness. Authentication, encryption, and categorization are the three components of this suggetsed method. In the first stage, the patient was verified using a substitution cipher and SHA-512. The patient was fitted with an Internet of Things(IoT) sensor wearable device, which was then affixed to his or her body to send the detected data, which was encrypted and transferred anonymously using PDH-AES protocols. DLMNN classifier was used to do classification once the digital information was decrypted. As a result, the data was divided into normal and abnormal categories, and an alarm message was sent to the doctor so that the patient could be treated. Results are confirmed and compared.

(Qureshi et al., 2022) presented the methods of self-monitoring wearable systems, mHealth and telemedicine strategy encourages people with cardiovascular illness to use wearable self monitoring technologies to cut down on hospitalizations. The incorporation of cutting edge methods like computer vision, learning techniques, and cloud services along with diagnostic systems were also discussed. Strengths and limitations of IoT applications in medical services were also identified. By this strategy the dependencies of patients on caregivers could be reduced.

(Tuli et al., 2020) health fog, a framework for integrating deep learning ensembles in to edge computing was devised and used to diagnose cardiac conditions in patients. Since the monitoring a programs need massive amount of data, fog and machine learning were made available in order to offer a novel answer. The term “healthcare fog” refers to the provision of healthcare through Internet of Things(IoT) devices and the administration of patient information.

(Poongodi et al., 2022a) In this research, we suggest a way to automatically suggest a suitable title and add a particular sound to the image. To get this result, two models that have been intensively trained together. The headlines are generated using a combination of natural language processing and cutting-edge computer vision models, and sounds are suggested based on the photographic scene.

(Hamdi et al., 2022) A new and improved CAD system based on a convolutional neural network (CNN) is created that can distinguish between people with normal cognitive function and those suffering from Alzheimer’s disease in order to prevent having a significant negative impact on how well earlier strategies are recognised. T the proposed approach is evaluated and the results indicated that the proposed CAD system provides an accuracy of 96.

(Poongodi et al., 2022b) To diagnose COVID-19, we presented a hybrid deep learning approach with layered technique is employed to assess the severity of the patients’ symptoms and determine whether they are positive for COVID-19 by examining the patient image data. In order to detect the coronavirus quickly and reliably in the lab, a deep learning model using the RNN and CNN algorithms is used to develop a coronavirus rapid test (Dhiman et al., 2022). Citrus fruits that have been labelled with the assistance of a domain expert with four severity levels (high, medium, low, and healthy) serve as the training data for the proposed deep neural network (DNN) model with VGGNet, which is trained to detect targeted parts of the disease with its severity degree.

(Poongodi et al., 2021a) This research assessed upcoming and prospective innovations that will enhance COVID-19 impacts for the general population. This can be done by applying artificial intelligence techniques such as Natural Language Processing (NLP), Machine Learning (ML), and Computer vision to a variety of processing files. This article intends to provide different sets of technologies that can be used to both get rid of COVID-19 and provide information for future generations.

(Kumar et al., 2022) This paper offers a preliminary and unavoidably partial look into the potential benefits of artificial intelligence in the fight against COVID-19 as well as its current difficulties. A number of AI applications, including as early warnings and alarms, monitoring and prediction, data dashboards, diagnosis and prognosis, medications and cures, and social control, may be useful in the battle against COVID-19. AI has the potential to be a very powerful tool in the battle against the COVID-19 virus.

(Bourouis et al., 2022) The computer-aided diagnosis method for identifying abnormalities in breast ultrasound pictures created by combining a wavelet neural network (WNN) with a grey wolf optimization (GWO) algorithm is the main innovation of this work. By calculating the confusion matrix and receiver operating characteristic (ROC) curve, the effectiveness of the suggested methodology is assessed. (Lilhore et al., 2022), (Poongodi et al., 2021b)

The suggested model was deployed using an IoMT-enabled cloud architecture, and its performance was verified using a variety of parameters, including those pertaining to throughput, energy consumption, precision,delay, processing time, and oscillation. As Healthcare IoMT could be configured in various modes of operation the best QoS or accuracy prediction was obtained as required based on requirements of users.

Here, we provide a high level overview of the methods that will be used to implement the plan. Diagram depicting the suggested method’s Figure 1. Cloud system acts as interconnection between the hospital and patients. In real time by the aid of wireless sensor network, smartphones and other digital devices can deliver the updates about patient’s health condition and risks through cloud network. If the patient’s condition is determined to be substandard as compared to the readings provided in lookup table, an automatic alert message will be generated to the physician that was stored in cloud system.

There are two sides of system in the cloud network that are server side and client side that includes various functions like online diagnosis, data querying and uploading patient data. In the server side, the web page is organised on the cloud whereas in the client side, intellectual terminals like medical devices and smart phones are utilized. In this system, the patient could login the web page and query about the status of health for precise period. Hence, the internet technology is integrated to the client for enhanced evaluation and medical data assessment.

The performance analysis of the proposed model is carried and the outcomes attained are compared with existing methods (Latif et al., 2022), (Khan et al., 2021), (Yang et al., 2017) and (Sharma et al., 2021b) to validate the efficiency of proposed methodology. The outcomes attained from the analysis is projected below in the table and graphical representation.

Table 2 shows that the performance parameters for simlation. The above parameters are recorded for 50 patients: respiration rate (RR), systolic blood pressure (SBP), heart rate (HR), oxygen saturation (OS), and blood sugar level (SL). Readings are gathered every second using biosensor sensors. A file with a log of each patient’s about 500 readings was used in our model. The biosensor sends the data to the caretaker after receiving it from its particular file on a regular basis.

Table 3 and Figure 2 signifies the performance comparison of accuracy, precision, recall, and F1-measure made for the proposed HRGC method and the existing methods like VGG16, AlexNet, and GoogleNet. From the analysis it was evident that the proposed model is better on comparing the traditional techniques.

Accuracy, precision, recall, F1-measure, and training duration are selected as measures by which the suggested technique may be effectively validated and the improvement of the proposed model proven.

From the outcome, it was observed that the accuracy level ranges from 97.32% to 98.71% in the existing models, whereas the proposed method offers highest range of accuracy 99.69%. Also, the proposed model offers high rate of precision (98%), recall (98%), and F1-measure (98.1%).

Table 4 and Figure 3 signifies the performance comparison of training time made for the proposed HRGC method and the existing methods like VGG16, AlexNet, and GoogleNet. From the analysis it was evident that the proposed model shows reduced training time on comparing the traditional techniques.

From the analysis, it was observed that the training time ranges from 25,568 to 54,866 s in case of existing models and about 49201 s in proposed model which is lower than existing ones.

Table 5 and Figure 4 signifies the performance comparison of accuracy and miss rate made for the proposed HRGC method and the existing methods like Logistic regression, XGBoost, RF, MSDNN, 3D Resnet, ANN and proposed HRGC. From the analysis it was evident that the proposed model offers high accuracy rate and lower miss rate on comparing the traditional techniques.

In table 6, the amount of time needed for computation is evaluated for the feature selection algorithm. The computational time is evaluated and the comparison is made. From the analysis, it was obvious that the proposed method offers lower computational time. Moreover, the fitness of proposed method was superior to other existing methods.

Table 7 and Figure 5 is the comparison made for various existing methods and the proposed model in terms of accuracy, recall, precision, and F1-measure. The proposed variant shows highest performance metrics on comparing other existing variants like combining DWT with FFBPNN, then CS-optimized DWT, and finally SVM-FFBPPNN, DWT optimized by CS, and subsequently HRGC’s suggested LDA.

Reliability, recall,accuracy, and F1-score are compared across several variations of both the current approaches and the proposed models in Table 8 and Figure 6. The primary goal of analyzing completion time is to catalog the change in runtime that corresponds to each improvement. The effectiveness of the suggested technique is shown by the shorter runtime of the proposed version compared to other variants.

At first, the patient data was collected from the patient body using sensed devices and the information were transmitted through gateway/Wi-Fi and is then kept in IoMT cloud repository. The data stored was acquired, preprocessed to denoise the collected data. The features were extracted using high dimensional LDA and the best optimal features are selected using reconfigured Multi-objective CSA. Then, the prediction of abnormal/normal data was made with the use of Hybrid ResNet 18 and GoogleNet classifier. The decision-making system decides whether to send alert or not based on the type of data predicted that is, normal or abnormal one. In case, there occurs some abnormality in the data predicted alter will be send to hospital/healthcare personnel. The performance assessment was made finally in terms of accuracy, miss rate, execution time, computation time, precision, recall, and F1-measure. From the estimation made, the outcomes attained are compared with traditional methods. It was revealed from the analysis that the proposed system functions well on comparing traditional methods. In future, this work can be extended by implementing video based system.