Smart Healthcare Framework Research Articles

Analyzing deep textual facial patterns for human pain sentiment recognition system in smart healthcare framework

BACKGROUND: Patient sentiment analysis aids in identifying issue areas, timely remediation, and improved patient care by the healthcare professional. The relationship between pain management and patient sentiment analysis is crucial to providing patients with high-quality medical care. Therefore, a self-reported pain level assessment is required for the smart healthcare framework to determine the best course of treatment. OBJECTIVE: An efficient method for a pain sentiment recognition system has been proposed based on the analysis of human facial emotion patterns of patients in the smart healthcare framework. METHODS: The proposed system has been implemented in four phases: (i) in the first phase, the facial regions of the observation patient have been detected using the computer vision-based face detection technique; (ii) in the second phase, the extracted facial regions are analyzed using deep learning based feature representation techniques to extract discriminant and crucial facial features to analyze the level of pain emotion of patient; (iii) the level of pain emotions belongs from macro to micro facial expressions, so, some advanced feature tunning and representation techniques are built along with deep learning based features such as to distinguish low to high pain emotions among the patients in the third phase of the implementation, (iv) finally, the performance of the proposed system is enhanced using the score fusion techniques applied on the obtained deep pain recognition models for the smart healthcare framework. RESULTS: The performance of the proposed system has been tested using two standard facial pain benchmark databases, the UNBC-McMaster shoulder pain expression archive dataset and the BioVid Heat Pain Dataset, and the results are compared with some existing state-of-the-art methods employed in this research area. CONCLUSIONS: From extensive experiments and comparative studies, it has been concluded that the proposed pain sentiment recognition system performs remarkably well compared to the other pain recognition systems for the smart healthcare framework.

An AIoT‐driven smart healthcare framework for zoonoses detection in integrated fog‐cloud computing environments

An AIoT‐driven smart healthcare framework for zoonoses detection in integrated fog‐cloud computing environments

SMARTSeiz: Deep Learning With Attention Mechanism for Accurate Seizure Recognition in IoT Healthcare Devices.

The Internet of Things (IoT) is capable of controlling the healthcare monitoring system for remote-based patients. Epilepsy, a chronic brain syndrome characterized by recurrent, unpredictable attacks, affects individuals of all ages. IoT-based seizure monitoring can greatly enhance seizure patients' quality of life. IoT device acquires patient data and transmits it to a computer program so that doctors can examine it. Currently, doctors invest significant manual effort in inspecting Electroencephalograph (EEG) signals to identify seizure activity. However, EEG-based seizure detection algorithms face challenges in real-world scenarios due to non-stationary EEG data and variable seizure patterns among patients and recording sessions. Therefore, a sophisticated computer-based approach is necessary to analyze complex EEG records. In this work, the authors proposed a hybrid approach by combining traditional convolution neural (CN) and recurrent neural networks (RNN) along with an attention mechanism for the automatic recognition of epileptic seizures through EEG signal analysis. This attention mechanism focuses on significant subsets of EEG data for class recognition, resulting in improved model performance. The proposed methods are evaluated using a publicly available UCI epileptic seizure recognition dataset, which consists of five classes: four normal conditions and one abnormal seizure condition. Experimental results demonstrate that the suggested approach achieves an overall accuracy of 97.05% for the five-class EEG recognition data, with an accuracy of 99.52% for binary classification distinguishing seizure cases from normal instances. Furthermore, the proposed intelligent seizure recognition model is compatible with an IoMT (Internet of Medical Things) cloud-based smart healthcare framework.

IPAL: A Machine Learning Based Smart Healthcare Framework for Automatic Diagnosis of Attention Deficit/Hyperactivity Disorder

iPAL: A Machine Learning Based Smart Healthcare Framework for Automatic Diagnosis of Attention Deficit/Hyperactivity Disorder

RETRACTED ARTICLE: Designing a cognitive smart healthcare framework for seizure prediction using multimodal convolutional neural network

RETRACTED ARTICLE: Designing a cognitive smart healthcare framework for seizure prediction using multimodal convolutional neural network

An IoT and Deep Learning-Based Smart Healthcare Framework for Thyroid Cancer Detection

A world of healthcare possibilities has been opened with the development of the Internet of Medical Things and related machine learning, deep learning, and artificial intelligence approaches. It has a broad range of uses: when linked to the Internet, common medical equipment and sensors may gather important data; deep learning and artificial intelligence algorithms use this data to understand symptoms and patterns and allow remote healthcare. There are a large number of people affected by thyroid disorders across the world. The ultrasound-based thyroid nodule detection using traditional methods increased the burden on the expertise. Therefore, alternate methods are required to overcome this problem. In order to facilitate early thyroid disorder detection, this research aims to offer an IoT-based ensemble learning framework. In the proposed ensemble model, three pre-trained models DeiT, Mixer-MLP and Swin Transformer, are used for feature extraction. The mRMR technique is used for relevant feature selection. A total of 24 machine learning models have been trained, and weighted average ensemble learning is employed using the Improved Jaya optimization algorithm and Coronavirus Herd Immunity optimization algorithm. The ensemble model with the improved Jaya optimization algorithm achieved excellent results. The best value for accuracy, precision, sensitivity, specificity, F2-score and ROC-AUC score are 92.83%, 87.76%, 97.66%, 88.89%, 0.9551 and 0.9357, respectively. The main focus of this research is to increase the specificity. A poor value of specificity can lead to a high false positive rate. This situation can increase anxiety and emotionally weaken the patient. The proposed ensemble model with the Improved Jaya optimization algorithm outperformed state-of-the-art techniques and can assist medical experts.

Accu-Help: A Machine-Learning-Based Smart Healthcare Framework for Accurate Detection of Obsessive Compulsive Disorder

Accu-Help: A Machine-Learning-Based Smart Healthcare Framework for Accurate Detection of Obsessive Compulsive Disorder

HealthFaaS: AI-Based Smart Healthcare System for Heart Patients Using Serverless Computing

Heart disease is one of the leading causes of death worldwide, and with early detection, mortality rates can be reduced. Well-known studies have shown that the latest Artificial Intelligence (AI) can be used to determine the risk of heart disease. However, existing studies did not consider dynamic scalability to get the best performance from these AI models in case of an increasing number of users. To solve this problem, we proposed an AI-powered smart healthcare framework called HealthFaaS, using the Internet of Things (IoT) and a Serverless Computing environment to reduce heart disease-related deaths and prevent financial losses by reducing misdiagnoses. HealthFaaS framework collects health data from users via IoT devices and sends it to AI models deployed on a Google Cloud Platform (GCP) based serverless computing environment due to its advantages such as dynamic scalability, less operational complexity, and a pay-as-you-go pricing model. The performance of five different AI models for heart disease risk detection is evaluated and compared based on key parameters such as accuracy, precision, recall, F-Score, and AUC. Experimental results demonstrate that the LightGBM model gives the highest success in detecting heart diseases with an accuracy rate of 91.80%. Further, we have tested the performance of the HealthFaaS framework in terms of Quality of Service (QoS) parameters such as throughput and latency against the increasing number of users and compared it with a non-serverless platform. In addition, we have also evaluated the cold start latency using a serverless platform which determined that the amount of memory and the software language makes a direct impact on the cold start latency.

Lightweight and smart data fusion approaches for wearable devices of the Internet of Medical Things

Internet of Medical Things (IoMT) refers to a group of dedicated devices, i.e., wearable sensors attached to the human body for capturing the real-time data, specifically physiological signals, to develop an automated and technology-assisted smart healthcare framework. However, due to the miniaturized nature and their limited capabilities, data captured by these wearable devices need to be consistent, especially in terms of accuracy and precision. Therefore, data must be refined prior to the actual processing, which is a challenging task in a smart healthcare infrastructure. In the literature, various approaches have been reported to address this issue. However, most of these approaches are either domain-specific or extremely difficult to implement, especially in the resource-constrained environments of IoMT. In this paper, we present two-tier lightweight device and server-enabled data fusion approaches for wearable devices and their server modules, respectively. In these approaches, each device is bound to perform an in-node processing, i.e., local data fusion, on the sensed data values prior to initialization of the transmission process. For this purpose, each wearable device is required to use knowledge of the previously transmitted refined data values. Additionally, the proposed approach is pruned against abrupt changing scenarios, e.g. heart attack, where the frequency of sensed data values drastically fluctuates. Moreover, a non-metric-based data fusion, i.e., global data fusion, is presented to enhance the accuracy of sensed data by removing redundant data values, especially those collected by neighboring devices. Simulation results reflect the exceptional performance of these smart data fusion approaches, i.e., both local and global, in the context of numerous performance evaluation metrics, such as accuracy and precision ratio.

TasLA: An innovative Tasmanian and Lichtenberg optimized attention deep convolution based data fusion model for IoMT smart healthcare

The Internet of Medical Things (IoMT) bolstered the smart health care industry in present times by enabling quicker patient monitoring and disease diagnosis. However, there have been problems that need to be resolved using Artificial Intelligence (AI) methods. The major goal of this endeavor is to develop an IoMT-based data fusion system for multi-sensor smart healthcare network. To do this, a new optimization and deep learning approaches are being used in this work. In this research work, a unique smart healthcare framework, Tasmanian and Lichtenberg Optimized Attention Deep Convolution (TasLA) is developed for IoMT systems. This system uses an intelligent data fusion algorithms for collecting of medical data and the diagnosis of disorders. Here, data pretreatment and normalization processes are carried out in order to provide a dataset with balanced attribute information. The qualities or characteristics that will aid in classification are then selected using the most modern Tasmanian Devil Optimization (TDO) approach. The Attention Deep Convolution Classification (ADCC) algorithm is also used to classify the medical condition, thereby improving classification precision and reducing false predictions. To optimally compute the loss function during prediction, the Lichtenberg Optimization (LO) technique is employed to enhance classification performance. The effectiveness and results of the proposed TasLA model are validated and contrasted using various benchmark datasets such as Hungarian, Cleveland, Echocardiogram, and Z-Alizadeh.

Secure Smart Healthcare Framework Using Lightweight DNA Sequence and Chaos for Mobile-Edge Computing

Mobile-edge computing (MEC) is a new architecture that provides services to the edge of networks. The software and hardware platforms are positioned at the network edge close to end-users. Emerging developments in MEC can be used for healthcare applications, such as remote patient monitoring, diagnosis, and treatment purposes. The remote access to the data can arise security and privacy issues. Unauthorized access or data leakage can hamper the complete security of the system. This makes the system inconvenient, untrusted, less suitable, and vulnerable. This article is aimed to propose a security framework for the privacy preservation of patient data in a MEC environment where the services are accessed at the network edge. A lightweight cryptographic technique is proposed by including a chaotic map and a DNA sequence of organisms for encryption of electronic health records (EHRs). The identity privacy will be maintained by using anonymous authentication. The framework’s performance is evaluated with memory usage and encryption time and found satisfactory results.

DepCap: A Smart Healthcare Framework for EEG Based Depression Detection Using Time-Frequency Response and Deep Neural Network

DepCap: A Smart Healthcare Framework for EEG Based Depression Detection Using Time-Frequency Response and Deep Neural Network

IoMT-Based Healthcare Framework for Ambient Assisted Living Using a Convolutional Neural Network

In the age of universal computing, human life is becoming smarter owing to the recent developments in the Internet of Medical Things (IoMT), wearable sensors, and telecommunication innovations, which provide more effective and smarter healthcare facilities. IoMT has the potential to shape the future of clinical research in the healthcare sector. Wearable sensors, patients, healthcare providers, and caregivers can connect through an IoMT network using software, information, and communication technology. Ambient assisted living (AAL) allows the incorporation of emerging innovations into the routine life events of patients. Machine learning (ML) teaches machines to learn from human experiences and to use computer algorithms to “learn” information directly instead of relying on a model. As the sample size accessible for learning increases, the performance of the algorithms improves. This paper proposes a novel IoMT-enabled smart healthcare framework for AAL to monitor the physical actions of patients using a convolutional neural network (CNN) algorithm for fast analysis, improved decision-making, and enhanced treatment support. The simulation results showed that the prediction accuracy of the proposed framework is higher than those of previously published approaches.

HealthEdge: A Machine Learning-Based Smart Healthcare Framework for Prediction of Type 2 Diabetes in an Integrated IoT, Edge, and Cloud Computing System

Diabetes Mellitus has no permanent cure to date and is one of the leading causes of death globally. The alarming increase in diabetes calls for the need to take precautionary measures to avoid/predict the occurrence of diabetes. This paper proposes HealthEdge, a machine learning-based smart healthcare framework for type 2 diabetes prediction in an integrated IoT-edge-cloud computing system. Numerical experiments and comparative analysis were carried out between the two most used machine learning algorithms in the literature, Random Forest (RF) and Logistic Regression (LR), using two real-life diabetes datasets. The results show that RF predicts diabetes with 6% more accuracy on average compared to LR.

Retracted: SmartMedChain: A Blockchain-Based Privacy-Preserving Smart Healthcare Framework.

[This retracts the article DOI: 10.1155/2021/4145512.].

A Novel Edge-Assisted IoT-ML-Based Smart Healthcare Framework for COVID-19

A Novel Edge-Assisted IoT-ML-Based Smart Healthcare Framework for COVID-19

A Smart Healthcare Framework for Accurate Detection of Schizophrenia Using Multichannel EEG

A Smart Healthcare Framework for Accurate Detection of Schizophrenia Using Multichannel EEG

A privacy preserving framework for federated learning in smart healthcare systems

Federated Learning (FL) is a platform for smart healthcare systems that use wearables and other Internet of Things enabled devices. However, source inference attacks (SIAs) can infer the connection between physiological data in training datasets with FL clients and reveal the identities of participants to the attackers. We propose a comprehensive smart healthcare framework for sharing physiological data, named FRESH, that is based on FL and ring signature defense from the attacks. In FRESH, physiological data are collected from individuals by wearable devices. These data are processed by edge computing devices (e.g., mobile phones, tablet PCs) that train ML models using local data. The model parameters are uploaded by edge computing devices to the central server for joint training of FL models of disease prediction. In this procedure, certificateless ring signature is used to hide the source of parameter updates during joint training for FL to effectively resist SIAs. In the proposed ring signature schema, an improved batch verification algorithm is designed to leverage additivity of linear operations on elliptic curves and to help reduce the computing workload of the server. Experimental results demonstrate that FRESH effectively reduces the success rate of SIAs and the batch verification method significantly improves the efficiency of signature verification. FRESH can be applied to large scale smart healthcare systems with FL involving large numbers of users.

Optimization technique based on cluster head selection algorithm for 5G-enabled IoMT smart healthcare framework for industry

Abstract Internet of medical things (IoMT) communication has become an increasingly important component of 5G wireless communication networks in healthcare as a result of the rapid proliferation of IoMT devices. Under current network architecture, widespread access to IoMT devices causes system overload and low energy efficiency. 5G-based IoMT systems aim to protect healthcare infrastructure and medical device functionality for longer. Therefore, using energy-efficient communication protocols is essential for enhancing QoS in IoMT systems. Several methods have been developed recently to improve IoMT QoS; however, clustering is more popular because it provides energy efficiency for medical applications. The primary drawback of the existing clustering technique is that their communication model does not take into account the chance of packet loss, which results in unreliable communication and drains the energy of medical nodes. In this study, we concentrated on designing a clustering model named Whale optimized weighted fuzzy-based cluster head selection algorithm to facilitate successful communication for IoMT-based systems. The experimental study shows that the proposed strategy performs better in terms of QoS than compared approaches. Inferring from this, the proposed method not only reduces energy consumption levels of 5G-based IoMT systems but also uniformly distributes cluster-head over a network to improve QoS.

Electronic Health Data Security in the Internet of Things through Watermarking: An Introduction

Patient digital records, known as electronic health data (EHD), have security and privacy concerns in the existing Internet of Things (IoT)-based healthcare systems. These issues are attracting much attention and need to be resolved urgently. This article aims to introduce IoT-enabled watermarking, addressing the ownership conflicts, and data security concerns of the smart healthcare framework. Further, a comprehensive review of current studies related to IoT-based healthcare with watermarking techniques, along with their merits and limitations, is provided. The contributions of the reviewed approaches are then summarized and compared from different technical perspectives. Lastly, recent challenges, along with several directions for potential research, are detailed and commented on.