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Hk Precise Prediction Application
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By Amani Aldahiri Amani Aldahiri Scilit Preprints.org Google Scholar View publications 1, * , Bashair Alrashed Bashair Alrashed Scilit Preprints.org Google Scholar View publications 1 and Walayat Hussain Walayat Hussain Scilit Preprints.org
School of Information, Systems and Models, Department of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia
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Received: January 18, 2021 / Revised: February 21, 2021 / Accepted: March 3, 2021 / Published: March 7, 2021
Machine learning (ML) is a powerful tool that provides insights hidden in Internet of Things (IoT) data. These hybrid technologies work intelligently to improve decision making in various fields such as education, security, business and healthcare. ML enables IoT to extract hidden patterns from large amounts of data for accurate prediction and recommendation applications. Healthcare has embraced IoT and ML so that autonomous machines can create medical records, predict diagnoses, and most importantly, perform patient diagnosis in real time. Individual ML algorithms perform differently on different data sets. Because the predicted outcomes vary, this may affect the overall results. The variability of predicted outcomes is large in the clinical decision-making process. Therefore, it is important to understand the different ML algorithms used to process IoT data in healthcare. This article highlights popular ML algorithms for classification and prediction and shows how they are used in healthcare. The purpose of this article is to provide a comprehensive overview of existing ML methods and their application to medical IoT data. On closer inspection, we find that different ML prediction algorithms have different problems. According to the nature of the IoT dataset, we need to choose the right method to predict the important health data. The article also provides some examples of IoT and machine learning to predict future trends in healthcare.
Predictive healthcare systems enable hospitals to promptly transfer outpatients to less busy treatment centers. They increase the number of patients who receive real medical care. Health prediction systems address the common problem of sudden changes in patient flow in hospitals. The need for medical services in many hospitals is caused by emergency situations such as the arrival of ambulances during natural disasters and traffic accidents, as well as the general need for outpatients . Hospitals without real-time patient flow data often struggle to meet demand, while nearby facilities may have fewer patients. The Internet of Things (IoT) creates connections between virtual computers and physical objects to facilitate communication. It enables instant data collection with new microprocessor chips.
It should be emphasized that nursing is to promote and maintain good health by detecting and preventing conditions. Abnormalities or cracks that appear under the skin’s surface can be examined with diagnostic tools such as SPECT, PET, MRI and CT. It is also possible to monitor certain rare conditions such as epilepsy and heart disease . Population growth and the unprecedented prevalence of chronic diseases have put pressure on today’s medical institutions. The overall demand for medical equipment, including nurses, doctors and hospital beds, is high . As a result, there is a need to reduce the pressure on health care systems while maintaining the quality and standards of health services . IoT offers possible measures to reduce the burden on healthcare facilities. For example, RFID systems are used in healthcare facilities to reduce healthcare costs and improve healthcare. In particular, doctors can easily monitor patients’ heart rate through health screening programs, allowing doctors to make an accurate diagnosis . Several portable devices have been developed to ensure consistent wireless data transmission. Despite the benefits of IoT in healthcare, IT professionals and healthcare professionals are concerned about data security . As a result, many studies have explored the integration of IoT and machine learning (ML) for diagnosing patients with medical conditions as a way to protect data integrity.
Machine Learning For Precision Medicine
IoT has ushered in a new era for the healthcare industry, allowing professionals to connect with patients. IoT uses machine learning to assess emergency care needs and determine a response plan at certain times of the year. Many outpatient clinics face the problem of overcrowding in waiting rooms . Patients visiting hospitals suffer from various conditions, some of which require urgent medical attention. The situation worsens when patients in emergency rooms have to wait in long queues. The problem is even worse in developing countries, where there is a shortage of hospital staff. Many patients often go home without medical help due to overcrowding in hospitals.
Yuvaraj and SriPreethaa developed a wearable medical sensor (WMS) platform with several applications and tools . The authors have thoroughly reviewed the use of WMS and its development and compared its performance with other platforms. The authors discussed the benefits of using these devices to monitor the health of patients with conditions such as heart attack and Alzheimer’s disease. Miotto et al. proposed a monitoring system based on wireless sensor networks (WSN) and fuzzy networks . In particular, the researchers combined microelectromechanical systems (MEMS) based with WSNs to create a physical sensor network (BSN) that constantly monitors abnormal changes in the health of patients. In particular, the authors developed a method to measure clinical data using devices such as microcontroller, heart rate and temperature . In addition, the proposed system was integrated with the center’s equipment to remotely control the patients’ heart rate and temperature and transfer the patient’s information to the doctor’s phone. In particular, the system can send SMS to the patient’s relatives and medical professionals in emergency . Therefore, patients can get a prescription remotely from this system.
In addition, the application of IoT has enabled hospitals to monitor the vital signs of patients with chronic diseases [11, 12]. The system uses this information to predict the health status of patients in various ways. IoT sensors are placed on the patient’s body to detect and identify their activity and predict the health status. For example, an IoT sensor system monitors diabetic patients to predict disease levels and any abnormal conditions in patients. Through the health forecasting system, patients can get suggestions of other hospitals where they can seek treatment. Those who do not want to go to other centers can choose to stay at the same center but face the possibility of standing in long queues or returning home untreated. Rajkomar and Dr.  proposed a healthcare platform with Zigbee and BSN technology for remote patient monitoring with clinical sensor data. In particular, they used parameters such as Zigbee IEEE 802.15.4 protocol, temperature indicators, spirometer data, heart rate and electrocardiogram to assess the health status of patients . The information obtained is transmitted via radio waves and displayed on visual devices, including desktop computers or mobile devices. Therefore, the proposed platform can monitor the patient’s characteristics, including temperature, glucose, respiration, EEG (electroencephalogram), ECG (electrocardiogram), and blood pressure (blood pressure), and transfer them to the database through Wi-Fi or GPRS. When sensor data is sent to Zigbee, it is sent to other networks, allowing it to be viewed by devices such as emergency medical equipment and cell phones of doctors and relatives . Therefore, the integration of IoT and machine learning simplifies the patient care system by improving the relationship between patients and doctors.
IoT provides patient management and monitoring systems through sensor networks with software and hardware. The latter includes devices such as Raspberry Pi board, blood pressure sensors, temperature sensors and heart rate sensors. The software process includes recording the sensor data, storing the data in the cloud, and analyzing the information stored in the cloud to assess the health vulnerability . However,
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