Predicting Chronic Disease by Monitoring Patients Updating Sensor Information with Big Health Application System (original) (raw)
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Medical Internet of Things and Big Data in Healthcare
sors, and network connectivity, which enables these objects to collect and exchange data . Its impact on medicine will be perhaps the most important, and personal, effect. By 2020, 40% of IoT-related technology will be health-related, more than any other category, making up a $117 billion market . The convergence of medicine and information technologies, such as medical informatics, will transform healthcare as we know it, curbing costs, reducing inefficiencies, and saving lives. illustrates how this revolution in medicine will look in a typical IoT hospital, in practice. A patient with diabetes will have an ID card that, when scanned, links to a secure cloud which stores their electronic health record vitals and lab results, medical and prescription histories. Physicians and nurses can easily access this record on a tablet or Objectives: A number of technologies can reduce overall costs for the prevention or management of chronic illnesses. These include devices that constantly monitor health indicators, devices that auto-administer therapies, or devices that track realtime health data when a patient self-administers a therapy. Because they have increased access to high-speed Internet and smartphones, many patients have started to use mobile applications (apps) to manage various health needs. These devices and mobile apps are now increasingly used and integrated with telemedicine and telehealth via the medical Internet of Things (mIoT). This paper reviews mIoT and big data in healthcare fields. Methods: mIoT is a critical piece of the digital transformation of healthcare, as it allows new business models to emerge and enables changes in work processes, productivity improvements, cost containment and enhanced customer experiences. Results: Wearables and mobile apps today support fitness, health education, symptom tracking, and collaborative disease management and care coordination. All those platform analytics can raise the relevancy of data interpretations, reducing the amount of time that end users spend piecing together data outputs. Insights gained from big data analysis will drive the digital disruption of the healthcare world, business processes and real-time decision-making. Conclusions: A new category of "personalised preventative health coaches" (Digital Health Advisors) will emerge. These workers will possess the skills and the ability to interpret and understand health and well-being data. They will help their clients avoid chronic and diet-related illness, improve cognitive function, achieve improved mental health and achieve improved lifestyles overall. As the global population ages, such roles will become increasingly important.
Digital Wellness: A Smart Health Care System Using Machine Learning
International Journal of Advances in Computer Science and Technology, 2021
Nowadays,people face various diseases due to environmental condition and their living habits. So the prediction of disease at an earlier stage becomes an important task. But the accurate prediction based on symptoms becomes too difficult for the doctor. The correctprediction of disease is the most challenging task. To overcome this problem data mining plays an important role to predict the disease. Medical science has a large amount of data growth per year. Due to the increasing amount of data growth in the medicaland healthcare field the accurate analysis of medical data has been benefits from early patient care. With the help of disease data, data mining finds hidden pattern information in a huge amount of medical data. We proposed general disease prediction based on the symptoms of the patient. For the disease prediction, we use Convolutional neural network (CNN) machine learning algorithm for the accurate prediction of disease. For disease prediction required disease symptoms dataset. After general disease prediction, this system able to gives the risk associated with a general disease which is a lower risk of general disease or higher.
Journal of Healthcare Engineering
A digital medical health system named Tianxia120 that can provide patients and hospitals with “one-step service” is proposed in this paper. Evolving from the techniques of Internet of Medical Things (IoMT), medical dig data, and medical Artificial Intelligence, the system can systematically promote the change of service status between doctors and patients from “passive mode” to “proactive mode” and realize online service that is similar to offline medical treatment scenarios. The system consists of a patient terminal and a doctor terminal. They can perform online inquiry (through graphic, voice, telephone, video, etc.), electronic prescription, multiparameter self-diagnosis, cold chain logistics, medicine distribution, etc. The system can provide rich medical health information, medical tools browsing, and health care big data aggregation processing functions. Compared with the traditional medical system, this system has the characteristics of full function, rich data, and high secu...
Iot Based Health Care System for Predicting Cardiac Issues
International Journal of Engineering and Advanced Technology, 2020
According to the survey conducted by the WHO (World Health Organization), out of 56.9 million deaths, Ischemic heart disease and Heart stroke account for 15.2 million deaths of the total deaths in 2016. These are regarded, as the Non-Communicable Diseases (NCD) also known as chronic diseases, tend to affect a person for a long duration. Along with in most of the cases, it is hard to find the disease existence in its primary stage; we can find it only with the symptoms like stroke or heart attack. Due to the lack of these symptoms, healthcare awareness and the financial needs many people are losing their lives. It is a very long process and cost effective. Hence, we are proposing a model, which predicts these symptoms with the implementation of latest technological advances like IoT, machine learning and deep learning algorithms. The proposed methodology takes place in three stages. Primary stage consists of collecting the data through sensors attached to the patient. Secondary stage...
Analyzing Healthcare Big Data With Prediction for Future Health Condition
IEEE Access
In healthcare management, a large volume of multi-structured patient data is generated from the clinical reports, doctor's notes, and wearable body sensors. The analysis of healthcare parameters and the prediction of the subsequent future health conditions are still in the informative stage. A cloud-enabled big data analytic platform is the best way to analyze the structured and unstructured data generated from healthcare management systems. In this paper, a probabilistic data collection mechanism is designed and the correlation analysis of those collected data is performed. Finally, a stochastic prediction model is designed to foresee the future health condition of the most correlated patients based on their current health status. Performance evaluation of the proposed protocols is realized through extensive simulations in the cloud environment, which gives about 98% accuracy of prediction, and maintains 90% of CPU and bandwidth utilization to reduce the analysis time.
Health prediction system using machine learning
International journal of health sciences
The emergence of the coronavirus (covid-19) pandemic has substantially elevated the worldwide demand for the healthcare system. Massive numbers of elderly and prone human beings are scuffling to fitness situations such as high blood pressure, diabetes, heart attack, and so on. Here in our project, I am making healthcare with the help of an algorithm and deep learning method to predict the disease. A user interacts with the system just like one interacts with his doctor and based on the symptoms provided by users and the system will identify the symptom and predict the disease. Thus, target to layout and implement a low- priced and smart healthcare system that allows non-stop assessment and tracking of patient fitness, thus BP and frame temperature monitoring is critical for that I used sensors that transmit information over a wi-fi network via a wi-fi module that allows fact analytics and visualization by using healthcare workforce.
Patient health monitoring using IoT with machine learning
2019
Health aspects of human being need to be monitored with utmost care and must be treated with appropriate drugs. Several diseases can be reduced by proactive monitoring of one's health. In the recent decades, technological development is at its peak due to which several wearable devices and health monitoring gadgets are available at the market. Even expert doctors find it challenging to estimate the health issues from the symptoms observed from the diseased. Using modern technological tools such as Internet of Things (IoT), machine learning and Artificial Intelligence along with Big data makes the job of physicians much easier in digging out the root cause of disease and predicting its seriousness using modern algorithms. In this research work, the machine learning algorithms are used to monitor the health conditions of the humans. Initial training and validation of machine learning algorithms are performed using the UCI dataset. Testing phase is carried out by collecting heart rate, blood pressure and temperature of the person using IoT setup. Testing phase estimates the prediction of any abnormalities in the health condition from the sensor data collected through the IoT framework. Statistical analysis is performed from data accumulated into the cloud from IoT device to estimate the accuracy in prediction percentage. Also, from the results obtained from the K-Nearest Neighbor outperforms other conventional classifiers.
Health Monitoring System Using IoT and Data Analysis
IJCSMC, 2019
In today’s lifestyle, Health has become a serious issue which directly affects quality of LIFE of a person. Health problems like cardiac failure, high blood pressure and diabetic patient needs to monitor problem on regular basis. This paper is based on monitoring of a person’s health condition automatically through sensors. Here, several sensors would be used for gathering the biological information of a person. Then, the information is forwarded to IOT. The system is more intelligent that can able to detect the critical condition of the patient .The analysis will be done on the data collected from patients of different age groups such as from 20-30, 30-40, 40-50 & so on. This data is represented in the graphical form. Graph will contain the data of different age group having average value of their biological information. And it will vary differently in Men and Women in each age group. This modern concept of monitoring the patient remotely will bring a major development in Medical arena.
Big data analytics and internet of things for personalised healthcare: opportunities and challenges
International Journal of Electrical and Computer Engineering (IJECE), 2023
With the increasing use of technologies and digitally driven healthcare systems worldwide, there will be several opportunities for the use of big data in personalized healthcare. In addition, With the advancements and availability of internet of things (IoT) based point-of-care (POC) technologies, big data analytics and artificial intelligence (AI) can provide useful methods and solutions in monitoring, diagnosis, and self-management of health issues for a better personalized healthcare. In this paper, we identify the current personalized healthcare trends and challenges. Then, propose an architecture to support big data analytics using POC test results of an individual. The proposed architecture can facilitate an integrated and self-managed healthcare as well as remote patient care by adapting three popular machine learning algorithms to leverage the current trends in IoT, big data infrastructures and data analytics for advancing personalized healthcare of the future. This is an open access article under the CC BY-SA license.
A Survey on Smart Digital Health Care Record with Prediction of Health Condition
2020
Humans are known to be the most intelligent species on the earth and are inherently more health conscious. Since Centuries mankind has discovered various healthcare systems. To automate the process and predict diseases more correctly machine learning methods are attending popularity in research community. We need to implement machine learning methodologies to identify the best-predicted values related to the patients in their respected health condition and also need to analyze the previous health records. For that, we need to maintain a repository or the warehouse where we need to maintain digital data related to the patients and their treatment.