Toward An IoT-based Expert System for Heart Disease Diagnosis

IoT technology has been recently adopted in the healthcare system to collect Electrocardiogram (ECG) signals for heart disease diagnosis and prediction. However, noises in collected ECG signals make the diagnosis and prediction system unreliable and imprecise. In this work, we have proposed a new lightweight approach to removing noises in collected ECG signals to perform precise diagnosis and prediction. First, we have used a revised Sequential Recursive (SR) algorithm to transform the signals into digital format. Then, the digital data is proceeded using a revised Discrete Wavelet Transform (DWT) algorithm to detect peaks in the data to remove noises. Finally, we extract some key features from the data to perform diagnosis and prediction based on a feature dataset. Redundant features are removed by using Fishers Linear Discriminant (FLD). We have used an ECG dataset from MIT-BIH (PhisioNet) to build a knowledge-base diagnosis features. We have implemented a proof-of concept system that collects and processes real ECG signals to perform heart disease diagnosis and prediction based on the built knowledge base

Big Data Analytics Based Data Driven Public Health Care System For Heart Disease Detection

Everyone should be more conscious of their lifestyle choices in order to live a healthy life. This can be done by following appropriate eating plans, being aware of healthcare issues, and knowing how to maintain good health. The health sector produces enormous amounts of electronic data every second in the present digital era, making it extremely difficult to store and manage the data using traditional software and technology. Additionally, the impending data is generated in varied forms, such as semi-structured, unstructured, and/or structured. The rate at which data are evolving and their abundance necessitate an overwhelming amount of health care data. Thus, it appears that health-related data can be handled using big data. This study demonstrates how quickly things are evolving, resulting in the creation of several approaches and continuous research into fixes for issues that crop up in a range of industries

Public Health Initiatives of Wearable Sensors for Health Monitoring and Early Heart Disease Detection

The science of preserving and enhancing individual and community health is known as public health. In order to accomplish this task, healthy lifestyle promotion, disease and injury prevention research, and the detection, prevention, and management of infectious diseases are used. Early detection and treatment of health disease can improve the prognosis for the condition worldwide. However, the massive volume of data needed poses a problem to the current automatic algorithms for diagnosing health illness. This study presents a medical gadget that uses the Internet of Things to gather cardiac data from individuals both before and after of heart illness. Technology is developing at a quick pace, leading to the establishment of many methodologies and ongoing research into solutions for problems that arise in a variety of industries. Preprocessing techniques are employed to effectively classify collected health data because the human body generates enormous amounts of data all the time. Furthermore, the most important phase is accurately classifying health data, which is necessary for diagnosis. The Deep Convolutional Neural Network (DCNN) is among the greatest and most efficient methods for categorising medical data. The results of the simulation in experimental research demonstrate that following this advise improves classification accuracy