Self Services And Monitoring Of Weak Heart Disease Based On The Internet Of Things And Mobile App Using Certainty Factor

Heart disease can be suffered by anyone regardless of age and gender. Heart disease can be caused by many things, such as unhealthy lifestyles (smoking), as well as heredity. Low self-awareness of someone with weak heart disease to monitor their heart health regularly. To diagnose weak heart disease, this system uses the Certainy Factor method. With this method, patients can find out the results of the decision whether the patient is at risk of developing weak heart disease or heart health conditions in normal circumstances. This method can provide comparative results (and decision results) of several parameters (tested), one of which is the heart rate parameter. Therefore, the authors build a mobile application system with the concept of service and monitoring of weak heart disease named "iHeart". With this application, patients can monitor and detect weak heart disease and use various features (features) contained in the application. One of the iHeart application facilities is the GUI Chart for heart rate monitoring. This application can also accommodate the patient's heart rate history data into a database as a data storage medium. This application system uses IoT technology, which makes it easy for users or patients and practitioners to see the results of a heartbeat to find out whether a patient has a weak heart or not, in real-time via a smartphone (smartphone) without interfering with patient mobility

An innovative IoT service for medical diagnosis

Due to the misdiagnose of diseases that increased recently in a scarily manner, many researchers devoted their efforts and deployed technologies to improve the medical diagnosis process and reducing the resulted risk. Accordingly, this paper proposed architecture of a cyber-medicine service for medical diagnosis, based internet of things (IoT) and cloud infrastructure (IaaS). This service offers a shared environment for medical data, and extracted knowledge and findings between patients and doctors in an interactive, secured, elastic and reliable way. It predicts the medical diagnosis and provides an appropriate treatment for the given symptoms and medical conditions based on multiple classifiers to assure high accuracy. Moreover, it entails different functionalities such as on-demand searching for scientific papers and diseases description for unrecognized combination of symptoms using web crawler to enrich the results. Where such searching results from crawler, are processed, analyzed and added to the resident knowledge base (KB) to achieve adaptability and subsidize the service predictive ability

Deep Learning-aided Brain Tumor Detection: An Initial ‎Experience based Cloud Framework ‎

Lately, the uncertainty of diagnosing diseases increased and spread due to the huge intertwined and ambiguity of symptoms, that leads to overwhelming and hindering the reliability of the diagnosis ‎process. Since tumor detection from ‎MRI scans depends mainly on the specialist experience, ‎misdetection will result an inaccurate curing that might cause ‎critical harm consequent results. In this paper, detection service for brain tumors is introduced as ‎an aiding function for both patients and specialist. The ‎paper focuses on automatic MRI brain tumor detection under a cloud based framework for multi-medical diagnosed services. The proposed CNN-aided deep architecture contains two phases: the features extraction phase followed by a detection phase. The contour ‎detection and binary segmentation were applied to extract the region ‎of interest and reduce the unnecessary information before injecting the data into the model for training. The brain tumor ‎data was obtained from Kaggle datasets, it contains 2062 cases, ‎‎1083 tumorous and 979 non-tumorous after preprocessing and ‎augmentation phases. The training and validation phases have been ‎done using different images’ sizes varied between (16, 16) to ‎‎ (128,128). The experimental results show 97.3% for detection ‎accuracy, 96.9% for Sensitivity, and 96.1% specificity. Moreover, ‎using small filters with such type of images ensures better and faster ‎performance with more deep learning.

Big data analytics and internet of things for personalised healthcare: opportunities and challenges

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

Mobilemicroservices Architecture for Remote Monitoring of Patients : A Feasibility Study

Recent developments in smart mobile devices (SMDs), wearable sensors, the Internet, mobile networks, and computing power provide new healthcare opportunities that are not restricted geographically. This paper aims to introduce Mobilemicroservices Architecture (MMA) based on a study on architectures. In MMA, an HTTP-based Mobilemicroservivce (MM) is allocated to each SMD's sensor. The key benefits are extendibility, scalability, ease of use for the patient, security, and the possibility to collect raw data without the necessity to involve cloud services. Feasibility was investigated in a two-year project, where MMA-based solutions were used to collect motor function data from patients with Parkinson's disease. First, we collected motor function data from 98 patients and healthy controls during their visit to a clinic. Second, we monitored the same subjects in real-time for three days in their everyday living environment. These MMA applications represent HTTP-based business-logic computing in which the SMDs' resources are accessible globally.publishedVersionPeer reviewe

Blockchain leveraged decentralized IoT eHealth framework

Blockchain technologies recently emerging for eHealth, can facilitate a secure, decentral- ized and patient-driven, record management system. However, Blockchain technologies cannot accommodate the storage of data generated from IoT devices in remote patient management (RPM) settings as this application requires a fast consensus mechanism, care- ful management of keys and enhanced protocols for privacy. In this paper, we propose a Blockchain leveraged decentralized eHealth architecture which comprises three layers: (1) The Sensing layer –Body Area Sensor Networks include medical sensors typically on or in a patient body transmitting data to a smartphone. (2) The NEAR processing layer –Edge Networks consist of devices at one hop from data sensing IoT devices. (3) The FAR pro- cessing layer –Core Networks comprise Cloud or other high computing servers). A Patient Agent (PA) software replicated on the three layers processes medical data to ensure reli- able, secure and private communication. The PA executes a lightweight Blockchain consen- sus mechanism and utilizes a Blockchain leveraged task-offloading algorithm to ensure pa- tient’s privacy while outsourcing tasks. Performance analysis of the decentralized eHealth architecture has been conducted to demonstrate the feasibility of the system in the pro- cessing and storage of RPM data

Addressing data accuracy and information integrity in mHealth using ML

The aim of the study was finding a way in which Machine Learning can be applied in mHealth Solutions to detect inaccurate data that can potentially harm patients. The result was an algorithm that classified accurate and inaccurate data

AI and Blockchain-assisted diagnostics in resource-limited setting

Diseases, including communicable and noncommunicable diseases, have been one of the major causes of human morbidity and mortality since the beginning of our history. Although many diseases have become treatable or preventable, thanks to interventions including pharmaceutical and technological advances, many people die each year in developing countries and remote rural areas due to limited (or even no) access to medical facilities and expertise. An accurate, rapid, and reliable diagnostic test is vital to improved disease treatment and prevention. However, running diagnostic tests usually requires complex, expensive instruments, professionally trained operators, and a stable power supply. Unfortunately, these resources are generally limited or unavailable in many low-resource settings. Although there are countless limitations in running diagnostic tests in low-resource settings, various endeavours have been made to overcome the existing obstacles. One of the most important advances has been the development of point-of-care or point-of-need tests. These diagnostic assays can be delivered in convenient formats and have successfully reduced the cost of running diagnostics, so playing an essential role in disease management and lifesaving in low-income countries. One key aspect of diagnosis may be the interpretation of the test, which can either be done by an expert in the field or by communicating that data to a remote expert or a “smart” system to interpret the data. Accurately interpreting the test outcome can help the patients receive appropriate treatment timely. However, issues presented in data management during such communication, such as tampered and counterfeited test results and unsecured data sharing between end users (patients) and professionals (doctors, healthcare workers, researchers, etc.). Also, problems like unreliable electricity supply and internet connection were found during the field study conducted by our group previously, and those issues can also delay the diagnosis of the disease. In this PhD study, an AI-assisted platform for DNA-based malaria diagnostic tests was developed and tested in the field. This platform allows users to run a test with a low-cost portable heater and record the test information with an Android phone. It can be used to run LAMP-based malaria tests with a portable heater and read the test results automatically with 97.8% accuracy. And it only takes around 20 milliseconds to classify one image on an inexpensive (~£100) Android phone. When the internet connection is available, the test information can be safely kept in a Blockchain network for future use to inform treatment or surveillance activities. Expertise developed in the deep neural network was also used to train algorithms for the diagnosis of retinopathies, involving developing methods for retina vessel segmentation and classification, which explores the possibility of applying AI to diagnostics in low-resource settings. In such settings, accessing medical expertise can be challenging. It has been found that using only a convolutional neural network is not sufficient in identifying arteries and veins. Models were trained for performing vessel segmentation and classification tasks; for segmenting vessels from the background achieved over 95% accuracy and over 0.8 mean average over the union score (MIoU) on the DRIVE dataset, while for A/V classification tasks, the MIoU decreased to less than 0.7. However, combining it with the traditional approach has the potential to achieve good performance. In addition, research was conducted on the utilisation of digital technologies to assist other researchers and engage with the public. To assist researchers in determining the minimum required sample size, a web-based calculator was developed during the COVID-19 pandemic. Furthermore, a website was created containing 360-degree images to help individuals comprehend the challenges of diagnostics and healthcare in developing regions and to raise awareness about how infectious diseases spread