Software Defined Radars for Low-Cost Healthcare Monitoring and Imaging Systems: A Comprehensive Review

The Internet of Things (IoT) is a newly emerging term for the new generation of the Internet which allows understanding between interconnected devices. IoT acts as an assistant in healthcare and plays an extremely important role in wide scopes of medicinal services observing applications. Through determining the pattern of parameters that are observed, the character of the disease can be expected. Health specialists and technicians have developed a great system with low-cost healthcare monitoring for people suffering from many diseases using common techniques such as wearable devices, wireless channels, and other remote devices. Network-related sensors, either worn on the body or in living environments, collect rich information to assess the physical and mental state of the patient. This work focuses on scanning the existing e-health (electronic healthcare) monitoring system using integrated systems. The main goal of the e-health monitoring system is to offer the patient a prescription automatically according to his or her condition. The doctor can check patient health continuously without physical interaction. The study aims to explore the uses of IoT applications in the medical sector, and its role in raising the level of medical care services in health institutions. Also, the study will address the applications of IoT in the medical field and the extent of its use to enrich traditional methods in various health fields and to determine the extent of the ability of IoT to improve the quality of health services provided. The study relies on a descriptive research approach through an analysis of the literature published in this field. The results of the study refer to the application of IoT in the health institutions, it will help to obtain accurate diagnoses for patients, which will reflect on the quality of service provided to the patient. It will also reduce periodic patient reviews to the hospital by relying on IoT applications for remote diagnosis. Also, an application in health institutions will contribute to providing data correct for the diseases that patients suffer from, and hence employing them in preparing scientific research to obtain more accurate results. This paper introduces the review of the Internet-based healthcare monitoring system (HCMS) and the general outlines on opportunities and challenges of the patient’s Internet-based patient health monitoring system.

Wireless body area networks (WBANs) are a research area that supports patients with healthcare monitoring. In WBAN, the Internet of Things (IoT) is connected with WBAN for a smart/remote healthcare monitoring system in which various medical diseases are diagnosed. Quality of service (QoS), security and energy efficiency achievements are the major issues in the WBAN-IoT environment. Existing schemes for these three issues fail to achieve them since nodes are resource constrained and hence delay and the energy consumption is minimized. In this paper, a blockchain-assisted delay and energy aware healthcare monitoring (B-DEAH) system is presented in the WBAN-IoT environment. Both body sensors and environment sensors are deployed with dual sinks for emergency and periodical packet transmission. Various processes are involved in this paper, and each process is described as follows: Key registration for patients using an extended version of the PRESENT algorithm is proposed. Cluster formation and cluster head selection are implemented using spotted hyena optimizer. Then, cluster-based routing is established using the MOORA algorithm. For data transmission, the patient block agent (PBA) is deployed and authenticated using the four Q curve asymmetric algorithm. In PBA, three entities are used: classifier and queue manager, channel selector and security manager. Each entity is run by a special function, as packets are classified using two stream deep reinforcement learning (TS-DRL) into three classes: emergency, non-emergency and faulty data. Individual packets are put into a separate queue, which is called emergency, periodical and faulty. Each queue is handled using Reyni entropy. Periodical packets are forwarded by a separate channel without any interference using a multi objective based channel selection algorithm. Then, all packets are encrypted and forwarded to the sink nodes. Simulation is conducted using the OMNeT++ network simulator, in which diverse parameters are evaluated and compared with several existing works in terms of network throughput for periodic (41.75 Kbps) and emergency packets (42.5 Kbps); end-to-end delay for periodic (0.036 s) and emergency packets (0.028 s); packet loss rate (1.1%); residual energy in terms of simulation rounds based on periodic (0.039 J) and emergency packets (0.044 J) and in terms of simulation time based on periodic (8.35 J) and emergency packets (8.53 J); success rate for periodic (87.83%) and emergency packets (87.5%); authentication time (3.25 s); and reliability (87.83%).

In this era of smart healthcare system, patient expects better healthcare support with low cost which satisfy through the innovative process such as Internet of Things (IoT), cloud computing and data science techniques. Meantime, the healthcare industry faces many problems including the data collection and storage for further progress. For healthcare monitoring system, the data collection and data analytics plays important role to screening the patient health. Therefore, data science techniques and cloud computing are heart for the healthcare system to resists several problems in terms of technical aspects. For further enhancement, efficient data science technique is proposed for IoT assisted healthcare monitoring (DST‐HM) system using cloud computing, which improves the data processing efficiency, data accessibility in cloud. The several IoT sensors are used in a person corpse to collect the real clinical data. The composed data are then maintained in cloud for added data science processing. In DST‐HM system, we first introduce a modified data science technique that is, improved pigeon optimization (IPO) algorithm for grouping the cloud stored data which enhances the prediction rate. Second, we illustrate the optimal feature selection technique for feature extraction and selection.

Nowadays, ageing related diseases represent one of the most relevant challenges for developed countries. The use of healthcare remote technology may allow reducing most of the management of the chronic diseases meanwhile it may also contribute to the improvement of elderly people's quality of life. Unfortunately, despite the advent of Internet of things and the even decreasing price of sensors, current proposals are not extensible during runtime meaning that they need to be maintained offline by engineers. Therefore, in this paper we discuss how to build an ad-hoc extensible (during runtime) healthcare monitoring system by using low cost wireless sensors and already existent Internet of things technology as communication platform. Moreover, we present a prototype of a basic healthcare remote monitoring system, which alerts, in real time, patients' relatives or medical doctors that an elderly people is experienced a problem that could need medical attention or hospitalization.

Cardiovascular disease is the most common killer worldwide. The technology advancement has successfully postponed someone obtaining cardiovascular disease and increased healthy life expectancy. Cardiovascular diseases are mostly affected by unhealthy life style, heredity or aging. The abnormalities of anatomy and physiology in cardiovascular system such as blood vessel blockage, valve defect, and abnormal heart muscle are typical characteristics of cardiovascular diseases. These abnormalities are happened in the DNA, cell, tissue, organ or body system. The system carries blood containing nutrient and oxygen via pulmonary and systemic circulation controlled by nerve system, protected by immune system and regulated by hormone system. The management of cardiovascular diseases is very complex task. In the early stage of abnormalities, a high performance medical diagnosis instrument is required. This applies also in the detail diagnosis, surgical or intervention, as well as post treatment monitoring. One of the best diagnosis modalities is the medical imaging system. A number of medical imaging systems have been invented since many decades. Until now, however there are still limitations which need to be solved to achieve a safe imaging system with excellent resolution and speed for time and spatial dependent imaging. Ultrasound system has limited resolution due to size of crystal and frequency used. Image quality of X-Ray Computer Tomography is influenced by radiation dose. Although MRI is expected to be the best imaging modalities do to safety and capability in differentiation between soft tissues, this modality needs very high performance parallel computing and high tesla magnetic field. Other modalities have been also used clinically such as infrared and microwave. These have however limited resolution. In the last few years, some imaging modalities have been combined, such as PET-CT, PET-MRI, Ultrasound-CT, and Magneto-Acoustics. The combination is to obtain benefit mixtures of each modality. Besides, it is also well known, the acoustics, magnetic, electromagnetic as well as ionizing radiation can be used for therapy of certain abnormalities such as cancer and cardiovascular plaque. The combination between therapy and diagnosis imaging is a new direction in the imaging. Based on the latest development of imaging system, it can be predicted that the future imaging system is very market dependent. There are two directions of future imaging development. First is a safe, low cost and fast imaging system for early detection and prevention. Second direction is a high resolution, and real time imaging system with therapeutic effect for detail diagnosis and treatment monitoring. Both imaging systems require the advancement in the area of nanotechnology, information technology and medical technology. New imaging transducers and detectors which able to produce high resolution images and cost effective are the target in the nanotechnology development. This includes the investigation of new sensors material and processing technique. High-Q polymer, controllable radioactive and high temperature superconductive materials are future key of imaging transducers. Nanotechnology contributes also to high speed image processing. Combination between nano, medical and information technologies will enable real time automatic detection of abnormalities. It is predicted in 20 years, low cost, safe and fast imaging system will replace the current stethoscope. By 2050, high resolution and real time multimodalities imaging in combination with intervention system will be used as the best treatment system to manage cardiovascular diseases.

To maintain a good health, appropriate health care monitoring system is required. Particularly in Bangladesh there has been noticed a great lacking in proper health care monitoring system. That is why in this paper, an automatic healthcare monitoring system has been designed and implemented as prototype. Temperature, pulse rate and QRS complex form ECG signal in domain has been taken into account as extracted feature for the analysis for determining the condition of the patient using a mobile application named as “Medicare”. The extracted features taken from different sensors will be transmitted to “Medicare” though the google firebase server. This is an easy, automated and user friendly health care monitoring mobile application which is developed on android platform. In this system, the hospital authority, doctors, guardians and relevant personnel can have the access of these data to monitor and to take immediate treatment for critical conditions to prevent the uncertain patient’s death and to save a lot of time for the doctors and nurses as well. The proposed prototype can be implemented successfully at different hospitals and it will certainly enrich the healthcare monitoring system in Bangladesh.

Microwave imaging has been investigated in the last few years as an attractive complement to current diagnostic tools for medical applications due to its low-cost, portability and non-ionization radiation. Research to verify the feasibility of microwave imaging systems for medical applications is conducted using a Vector Network Analyser (VNA) as the microwave transceiver. The VNA is usually bulky and expensive, and thus prevents microwave imaging systems from being low-cost and portable. A necessary condition to turn microwave imaging into a mass screening diagnostic tool is to replace the VNA with a low-cost portable unit that can characterise, generate, transmit and receive signals, across a wideband with large dynamic range and stability. Tissues in the human body are lossy at microwave frequencies, and hence microwave signals undergo high attenuation when penetrating the human tissues during the process of imaging. Using a wider frequency spectrum provides better resolution, but low frequencies penetrate further into the body. As a trade-off between the required signal penetration and image resolution, microwave frequencies within the band 0.5-4 GHz have been used in many medical applications, such as head, torso, and breast imaging. Thus, the desired generic microwave transceiver for microwave-based medical imaging should cover this wide frequency band. This thesis proposes two versions of a reconfigurable low-cost portable broadband RF frontend medical imaging systems based on Software Defined Radio (SDR) and in doing so makes four contributions to the field of microwave imaging systems. The first contribution is the design of a low-cost reconfigurable microwave transceiver based on software defined radar (SDRadar). A RF broadband circulator used to separate the transmitted and received signal and a virtual ultra-wideband (UWB) time domain pulse is generated by coherently adding multiple frequency spectrums together. To verify the proposed SDRadar system for medical imaging, experiments were conducted using a circular scanning system and directional antenna. An image reconstruction algorithm used to generate and verify the images of the target embedded in a phantom developed with liquid emulating the average properties of different human tissues using the measurement data. The system successfully detects and localise small targets at different locations in the phantom. The above broadband circulator, however, limited the isolation between the transmitted and received signal, and the system lacked a calibration process. Hence, the proposed system is unable to image more complex human tissues. The second contribution of the thesis is the design of Vector Network Analyzer (VNA) by using the above SDR called Software defined VNA (SDVNA) with a highly directive broadband directional coupler (0.5-4 GHz). However, using a directional coupler with a single-receiver single-transmitter the conventional open/short/match/thru calibration technique is not applicable, and thus, it is re-developed to take into account the SDVNA system’s architecture. The proposed SDVNA is capable of covering the band from 0.8 –3.8 GHz with a dynamic range of 80dB. The performance is verified by showing that the S-parameters of 1-port (antenna) and 2-ports (filter) as devices under test have close agreement to results from a commercial VNA. The system was further verified by performing microwave imaging of a head phantom with realistic permittivity and conductivity. The system was able to detect the location of an embedded bleeding in the realistic head phantom, with comparable quality to a commercial VNA, and significantly improved image compared with the previous circulator-based design. The third contribution of the thesis is the investigation of measurement accuracy of the proposed SDVNA and SDRadar for medical imaging. Measurement variation and repeatability is characterized and analysed. Measurement accuracy is essential for accurate image reconstruction. The proposed SDVNA system provides the measurement repeatability within 95% of a commercial VNA, enabling the system to be developed in the future as a low-cost portable system to be used in any clinic to detect a strokes in the brain. The fourth contribution of the thesis is the complete development of the low-cost portable multistatic head imaging system by using the above SDVNA and a helmet with array of eight antennas. In order to overcome the errors caused by antenna differences and antenna misalignment, a new calibration technique for arrays of the antennas proposed. Three known calibration standard based on free space/oil/water is developed and used effectively to eliminate the systematic error due to antenna differences and misalignment. Through the experimental analysis it was shown that the use of the proposed calibration techniques improves the location and detection accuracy of the system. The proposed low-cost portable system has a complete built in calibration techniques and auto measurement system to scan the complete head of the patient with strokes. This thesis describes the complete development of a low-cost portable medical imaging system for applications which can be used by medical professionals as a complementary imaging system to detect and localised abnormalities in human tissues.

Healthcare monitoring system in hospitals and other health institutes has increased significantly. In recent days healthcare monitoring systems with new technologies are becoming of great concern to countries all over the world. Nowadays IOT (internet of things) is such an emerging technology. Iot consists of various sensors and communication devices which are necessary tools for IOT based health monitoring systems. Among the various applications that Internet of Things (IoT) facilitated to the world, Health care and Health monitoring applications are most important. It also responded if any medical emergency was needed. IoT (Internet of Things) has brought a remarkable approach in healthcare during the global pandemic. The remote health surveillance monitors certain parameters of a patient using digital technology and allows a correct evaluation of health right at home. This brilliant internet revolution has not only minimized patient movement during COVID but also ensured smart healthcare for all ages. IoT heads to set up a strategic connection between the patient & the doctors. The goal is to track the important health parameters such as - Blood pressure, heart rate, blood glucose level etc. and evaluate the data to figure out any sort of medical emergency. From diagnosing heart disease to finding ICU bed availability in the nearest hospitals, digital technology is all set to assist. Using certain medical sensor devices & web-based apps, the health data is passed to the portal from where concerned doctors can provide medical assistance. The data collection, transmission & visualization can use a smartphone as a hub, thus making the operation smooth & flexible. It is the most practical & economical solution to people of all ages while avoiding direct contact & preventing the spread of the virus. The wearable real-time health tracking devices privilege the elder citizen through continuous monitoring while ensuring immediate measures in case of emergency. In this paper, a review of IoT based smart health monitoring systems is presented. The latest innovative technologies used for IoT based smart health monitoring systems with their benefits and challenges have been discussed. This review's goal is to effectively and continuously monitor the multiple patients in a hospital ward and as well as remotely located patients so that it will ultimately reduce hospital operating costs, all other communication costs and improve the quality of health service.

The improvement in the technology in health care monitoring system it is necessary to constantly monitor the patient's physiological parameter. In the advanced in the health care environment by the usage of IOT technology bring convenience to the patient and the physician. Since they are used in various medical areas. The internet of things (IOT) has been broadly used to interconnect the available medical resources and offers the smart, reliable and effective health care services. A sensor node has been attached on the surface of the body the sensor node will gather the information and sends them to the wireless sensor node. The sensor nodes which are arranged on the patient body can sense the heart beat, temperature of surrounding. The main objective of this system is to transmit the patient's health monitoring system through wireless communication in an emergence situation. We propose a secure IOT based healthcare monitoring system to check the heartbeat rate, temperature of the surrounding and saline level of the patient. In the proposed system of health care monitoring system the heart beat rate, temperature of surrounding and MEMS accelerometer is used for when a patient's body will fall down on the foor then it will send the SMS alert to the predefned mobile number and by using IOT we can send the data to the server.

Internet of Things (IoT) technology helped the development of E-Health care Monitoring System from direct visit to virtual Monitoring (Telemedicine). Smart health care system in IoT environment monitored the basic health signs such as heartbeat rate, body temperature and blood pressure in real-time applications. IoT and big data is a prominent challenge in smart healthcare system. In this health monitoring system big data is employed to analyze the large volume of data and to determine the Normal and Abnormal patient condition. Numerous issues like accuracy, time and error have yet to be conveyed to generate a ductile system for health care monitoring. To address these issues, I proposed a method called Theil Sen Linear Regression and Canopy Hopkins Statistics Clustering (TSLR-CHSC) for IoT based Health monitoring system is proposed. This method splits into three sections such as Data collection, Feature Selection and Clustering. First, Cardiovascular disease dataset is acquired from sensors are collected. Second, appropriate features can be selected by using Theil Sen regression feature. Third, clustering is performed based on the cluster tendency by using canopy algorithms. Through this way I deployed an efficient E-Health Monitoring System with minimum time consumption. For evaluation, a cardiovascular disease dataset is obtained from various medical sensor devices are analyzed to identify the disease severity. Keyword: IoT, Big Data, Theil Sen Linear Regression, Canopy Hopkins Statistic Clustering, Health Care Monitoring.

The main focus of this paper is to design a prototype model such that it embeds different body sensors to measure parameters like body temperature, heart beat rate etc., We used an Arduino UNO board, body sensors like Heartbeat sensor and Temperature sensor to analyze the inputs from the patient’s body and if there are any abnormalities regarding the pulse rate and temperature of the patient the monitoring system would give an alarm and display it on LCD. These processed values are recorded online and stored in the cloud database like Thingspeak and can be accessed through any android mobile or computer systems from any corner of the globe. This system is more useful in transport departments, especially in Buses as a very efficient and dedicated driver healthcare monitoring system. By using this model we can help all drivers who are dedicated to their work and need to be monitored due to their abnormal health conditions. Thus helping them in avoiding fatal accidents and saving the valuable lives of passengers riding along with them in the bus.

In this paper, a wearable/foldable super wideband (SWB) monopole antenna on a flexible liquid crystal polymer (LCP) substrate is implemented using surface micromachining for imaging, sensing, radar and healthcare monitoring systems. The antenna consists of a coplanar waveguide (CPW)-fed circular patch as a main radiator, a tapered transition portion between the patch and the CPW line, and corner-rounded ground planes, for broad bandwidth. The overall size of the antenna is 37.5 × 35.5 × 0.05 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> . The fabricated SWB antenna is characterized in a frequency spectrum from 1 to 50 GHz. A measured ratio bandwidth of 11.9:1 (3.0 to 35.3 GHz or 169% bandwidth) has been obtained. The antenna has a monopole-like radiation pattern and a group delay of less than ±1 ns across all spectrum ranges. The measured results show good agreement with those of simulation.

Clinicians and other health care providers are currently being expected to make an increasing number of consecutive and complex decisions based on a very large amount of complex data collected from a variety of heterogeneous sources, often produced asynchronously. This is complicated by the addition of a growing number of new diagnostic and monitoring devices further highlighting the potential for an ever-growing data stream as well as the challenge of diagnosing and treating multiple patients at one time. It can be argued that there is much hidden knowledge in various clinical data such as images, physiologic signals, and others that simply cannot be rapidly extracted by the human eye. During the last decade, the need for computational methods, in particular signal and image processing algorithms, to analyze these complex data sets and provide health care providers with recommendations and/or predictions has been further highlighted. However, due to the size and complexity of the data produced by monitoring and imaging systems, the need for more effective methods to extract knowledge from these images has not grown with the same rate. This, of course, will impede the development of clinical decision support systems. This special issue of Computational and Mathematical Methods in Medicine serves as a brief update to the current status of and advances in methods and approaches in biomedical signal and image processing methods used for clinical decision support systems. The computational methods presented in this special issue cover a wide spectrum of algorithmic approaches applied to a wide range of clinical applications. The paper by X. Li et al. provides a system to identify patients with poststroke mild cognitive impairment by pattern recognition of working memory load-related ERP which may improve our assessment of some of the long-term impacts of stroke. E. Swanly et al. present a methodology to process CT images in order to spot pulmonary TB in a more effective manner. F. Li and F. Porikli give the description of their method that allows tracking of lung tumors in orthogonal X-rays. The paper by N. Saidin et al. presents a method for computer-aided detection of breast density for more accurate detection of breast cancer. This paper also presents a methodology for visualization of other breast anatomical regions on mammogram. H. Jiang et al. provide a hybrid method, based on level-set methods, for extraction of pancreas images from CT scanning with may clinical applications where CT is used for detection of potential damages to pancreas. The study by M. Jiang et al. focuses on parameter optimization for support vector regression in solving the inverse ECG problem, while H.-T. Wu et al. present the results of their study on quantification of the complex fluctuation between R-R intervals series and photoplethysmography amplitude series. Both of these methods may have wide ranging implications for new physiologic diagnostic approaches for patients. The paper presented by Y.-W. Chen et al. focuses on a computer-aided diagnosis and quantification of cirrhotic livers based on morphological analysis and machine learning. I. Cruz-Aceves et al. present their unsupervised cardiac image segmentation that applies multiswarm active contours with a shape prior. Finally, the paper by H. Jiang et al. provides a liver segmentation method, based on snakes model and improved GrowCut algorithm that is applied to abdominal CT images. As more advanced imaging and monitoring systems are designed, it is expected that the algorithms to process the data produced by these systems need to be evolved accordingly. These novel approaches may not only help extract new knowledge that are not readily possible through current traditional interpretation but also set the stage for providing rapid predictive information assisting health care providers in making better informed decisions. As shown in the papers presented in this special issue, these changes need to address both size and complexity of the produced data. The opportunities are rich as are the challenges. Kayvan Najarian Kevin R. Ward Shahram Shirani

Clinicians and other health care providers are currently being expected to make an increasing number of consecutive and complex decisions based on a very large amount of complex data collected from a variety of heterogeneous sources, often produced asynchronously. This is complicated by the addition of a growing number of new diagnostic and monitoring devices further highlighting the potential for an ever-growing data stream as well as the challenge of diagnosing and treating multiple patients at one time. It can be argued that there is much hidden knowledge in various clinical data such as images, physiologic signals, and others that simply cannot be rapidly extracted by the human eye. During the last decade, the need for computational methods, in particular signal and image processing algorithms, to analyze these complex data sets and provide health care providers with recommendations and/or predictions has been further highlighted. However, due to the size and complexity of the data produced by monitoring and imaging systems, the need for more effective methods to extract knowledge from these images has not grown with the same rate. This, of course, will impede the development of clinical decision support systems. This special issue of Computational and Mathematical Methods in Medicine serves as a brief update to the current status of and advances in methods and approaches in biomedical signal and image processing methods used for clinical decision support systems. The computational methods presented in this special issue cover a wide spectrum of algorithmic approaches applied to a wide range of clinical applications. The paper by X. Li et al. provides a system to identify patients with poststroke mild cognitive impairment by pattern recognition of working memory load-related ERP which may improve our assessment of some of the long-term impacts of stroke. E. Swanly et al. present a methodology to process CT images in order to spot pulmonary TB in a more effective manner. F. Li and F. Porikli give the description of their method that allows tracking of lung tumors in orthogonal X-rays. The paper by N. Saidin et al. presents a method for computer-aided detection of breast density for more accurate detection of breast cancer. This paper also presents a methodology for visualization of other breast anatomical regions on mammogram. H. Jiang et al. provide a hybrid method, based on level-set methods, for extraction of pancreas images from CT scanning with may clinical applications where CT is used for detection of potential damages to pancreas. The study by M. Jiang et al. focuses on parameter optimization for support vector regression in solving the inverse ECG problem, while H.-T. Wu et al. present the results of their study on quantification of the complex fluctuation between R-R intervals series and photoplethysmography amplitude series. Both of these methods may have wide ranging implications for new physiologic diagnostic approaches for patients. The paper presented by Y.-W. Chen et al. focuses on a computer-aided diagnosis and quantification of cirrhotic livers based on morphological analysis and machine learning. I. Cruz-Aceves et al. present their unsupervised cardiac image segmentation that applies multiswarm active contours with a shape prior. Finally, the paper by H. Jiang et al. provides a liver segmentation method, based on snakes model and improved GrowCut algorithm that is applied to abdominal CT images. As more advanced imaging and monitoring systems are designed, it is expected that the algorithms to process the data produced by these systems need to be evolved accordingly. These novel approaches may not only help extract new knowledge that are not readily possible through current traditional interpretation but also set the stage for providing rapid predictive information assisting health care providers in making better informed decisions. As shown in the papers presented in this special issue, these changes need to address both size and complexity of the produced data. The opportunities are rich as are the challenges. Kayvan Najarian Kevin R. Ward Shahram Shirani

The healthcare monitoring systems, with the concept called ‘telehealth’, started early in the year 1948. In classical times, it was a challenge to connect the security and sustainability of the healthcare monitoring system. In the later years, introducing the Internet of Things (IoT) technology facilities into the world has helped the healthcare industry overcome the challenge. In IoT, the sensors are used to capture data in health monitoring systems. In this paper, a health monitoring system that combines the DHT11, MLX90614, AD8232, and MAX30100 sensors with an interpretable linear regression model to predict health risks is presented. The cross-validation is done for predictive performance, having R2 = 0.96. In Healthcare IoT, devices like wearable sensors and patient monitors are wirelessly connected. These networks often use mobile ad-hoc communication models. The health monitoring systems share vast quantities of data. Malicious nodes in the network can alter patient data and delay emergency messages. Here, the security and sustainability of the healthcare monitoring system are discussed, and the hybrid method is introduced, used for network security. The hybrid model is compared with existing models. The hybrid model with a large node number has better performance in PDR gain than LVT-CBGR by 6.04%. The QoS (Quality of Service) parameters for the ratio of packet delivery, latency, detection rate, overhead, and throughput are computed.