IoT Based Smart Monitoring of Patients’ with Acute Heart Failure

Abstract

The prediction of heart failure survivors is a challenging task and helps medical professionals to make the right decisions about patients. Expertise and experience of medical professionals are required to care for heart failure patients. Machine Learning models can help with understanding symptoms of cardiac disease. However, manual feature engineering is challenging and requires expertise to select the appropriate technique. This study proposes a smart healthcare framework using the Internet-of-Things (IoT) and cloud technologies that improve heart failure patients’ survival prediction without considering manual feature engineering. The smart IoT-based framework monitors patients on the basis of real-time data and provides timely, effective, and quality healthcare services to heart failure patients. The proposed model also investigates deep learning models in classifying heart failure patients as alive or deceased. The framework employs IoT-based sensors to obtain signals and send them to the cloud web server for processing. These signals are further processed by deep learning models to determine the state of patients. Patients’ health records and processing results are shared with a medical professional who will provide emergency help if required. The dataset used in this study contains 13 features and was attained from the UCI repository known as Heart Failure Clinical Records. The experimental results revealed that the CNN model is superior to other deep learning and machine learning models with a 0.9289 accuracy value.

Smart Home Technology Solutions for Cardiovascular Diseases: A Systematic Review

“…Globally, HF is rising in prevalence; however, there are several unmet severe social and healthcare needs for both patients and caregivers, necessitating research to develop and evaluate disease management toolkits [84]. A smart healthcare framework using IoT, and cloud technologies could monitor HF patients based on real-time data and provides timely, effective, and quality healthcare services [85]. However, although several prototypes have been developed to monitor HF patients, there is a need to carry out future clinical trials, including those targeting a reduction in HF hospitalisations [20].…”

Monitoring Acute Heart Failure Patients Using Internet-of-Things-Based Smart Monitoring System

“…When we are talking about training sets that are not large, easy implementation, speed, and the quick-result Multi-Layer Perceptron are the best choice [ 12 ]. The internal structure of the MLP comprises three layers, input, output, and hidden layers.…”

“…When we are talking about sequential neural networks, the Recurrent Neural Network (RNN) is the best choice [ 12 ]. During processing, the input sequence of one neuron is fed to other neurons in the same weighted sequence of words in a sentence.…”

“…Some efforts have been made in the recent past to design a smart bed concept and smart point-of-care (PoC) devices, but due to population growth, further efforts are needed. According to the world health organization (WHO), 17.9 million deaths are recorded yearly because of cardiac diseases [ 12 ]. Heart disease is a major challenge in the medical field.…”

Classification and Diagnosis of Heart Disease Using Machine Learning

“…The efficiency of the suggested heterogeneous hybrid feature selection strategy is demonstrated by their findings. In [15], a smart healthcare framework that improves the survival prognosis for heart failure patients without considering human feature engineering was proposed. Cloud computing and Internet of Things (IoT) technologies are used in this framework.…”