DEEP-CARDIO: Recommendation System for Cardiovascular Disease Prediction Using IoT Network

IEEE Sensors Journal, Journal Year: 2024, Volume and Issue: 24(9), P. 14539 - 14547

Published: March 14, 2024

The Internet of Things (IoTs)-based remote healthcare applications provide fast and preventative medical services to the patients at risk. However, predicting heart disease is a complex task, diagnosis results are rarely accurate. To address this issue, novel Recommendation System for Cardiovascular Disease (CVD) Prediction Using IoT Network (DEEP-CARDIO) has been proposed providing prior diagnosis, treatment, dietary recommendations cardiac diseases. Initially, physiological data collected from remotely by using four biosensors, such as ECG sensor, pressure pulse glucose sensor. An Arduino controller receives sensors predict diagnose disease. A CVD prediction model implemented bidirectional-gated recurrent unit (BiGRU) attention model, which diagnoses classifies into five available cardiovascular classes. recommendation system provides physical based on classified data, via user mobile application. performance DEEP-CARDIO validated Cloud Simulator (CloudSim) real-time Framingham's Statlog dataset. DEEP CARDIO method achieves an overall accuracy 99.90%, whereas MABC-SVM, HCBDA, MLbPM methods achieve 86.91%, 88.65%, 93.63%, respectively.

Language: Английский

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Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions

Future Internet, Journal Year: 2023, Volume and Issue: 15(11), P. 370 - 370

Published: Nov. 18, 2023

Edge AI, an interdisciplinary technology that enables distributed intelligence with edge devices, is quickly becoming a critical component in early health prediction. AI encompasses data analytics and artificial (AI) using machine learning, deep federated learning models deployed executed at the of network, far from centralized centers. careful analysis large datasets derived multiple sources, including electronic records, wearable demographic information, making it possible to identify intricate patterns predict person’s future health. Federated novel approach further enhances this prediction by enabling collaborative training on devices while maintaining privacy. Using computing, can be processed analyzed locally, reducing latency instant decision making. This article reviews role highlights its potential improve public Topics covered include use algorithms for detection chronic diseases such as diabetes cancer computing detect spread infectious diseases. In addition discussing challenges limitations prediction, emphasizes research directions address these concerns integration existing healthcare systems explore full technologies improving

Language: Английский

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FedEHR: A Federated Learning Approach towards the Prediction of Heart Diseases in IoT-Based Electronic Health Records

Diagnostics, Journal Year: 2023, Volume and Issue: 13(20), P. 3166 - 3166

Published: Oct. 10, 2023

In contemporary healthcare, the prediction and identification of cardiac diseases is crucial. By leveraging capabilities Internet Things (IoT)-enabled devices Electronic Health Records (EHRs), healthcare sector can largely benefit to improve patient outcomes by increasing accuracy disease prediction. However, protecting data privacy essential promote participation adhere rules. The suggested methodology combines EHRs with IoT-generated health predict heart disease. For its capacity manage high-dimensional choose pertinent features, a soft-margin L1-regularised Support Vector Machine (sSVM) classifier used. large-scale sSVM problem successfully solved using cluster primal–dual splitting algorithm, which improves computational complexity scalability. integration federated learning provides cooperative predictive analytics that upholds privacy. use framework in this study, focus on peer-to-peer applications, crucial for enabling collaborative modeling while confidentiality each participant’s private medical information.

Language: Английский

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An Enhanced IOMT and Blockchain‐Based Heart Disease Monitoring System Using BS‐THA and OA‐CNN

Transactions on Emerging Telecommunications Technologies, Journal Year: 2025, Volume and Issue: 36(2)

Published: Feb. 1, 2025

ABSTRACT The heart disease monitoring system is helpful for doctors to understand the overall health of patients by measuring functions via IoMT devices. However, existing studies did not consider arrhythmias' consequences along with ECG and PCG predict accurately. Therefore, this paper presents an enhanced blockchain‐based using BS‐THA OA‐CNN. doctor patient can initially register log in system. At point, keys are generated doctors. After login, data sensing done, sensed uploaded IPFS. Next, hashcode stored blockchain. In meantime, MAC created verified authentication. verifying MAC, given classification system, which trained based on preprocessing, spectrum analysis, signal decomposition PV‐EMD, scalogram, grayscale conversion, wavelet components extraction, wave intervals arrhythmia consequences, feature extraction DPCA, selection, classification. Finally, proposed OA‐CNN effectively classified disease. Thus, results proved that methodology achieved a higher accuracy 98.32%, better than prevailing models.

Language: Английский

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Asynchronous Federated Learning for Improved Cardiovascular Disease Prediction Using Artificial Intelligence

Diagnostics, Journal Year: 2023, Volume and Issue: 13(14), P. 2340 - 2340

Published: July 11, 2023

Healthcare professionals consider predicting heart disease an essential task and deep learning has proven to be a promising approach for achieving this goal. This research paper introduces novel method called the asynchronous federated cardiac prediction (AFLCP), which combines dataset neural networks (DNNs) with technique. The proposed employs asynchronously updating parameters of DNNs incorporates temporally weighted aggregation technique enhance accuracy convergence central model. To evaluate effectiveness AFLCP method, two datasets various DNN architectures are tested, results demonstrate that outperforms baseline in terms both communication cost model accuracy.

Language: Английский

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Federated Machine Learning for Skin Lesion Diagnosis: An Asynchronous and Weighted Approach

Diagnostics, Journal Year: 2023, Volume and Issue: 13(11), P. 1964 - 1964

Published: June 5, 2023

The accurate and timely diagnosis of skin cancer is crucial as it can be a life-threatening disease. However, the implementation traditional machine learning algorithms in healthcare settings faced with significant challenges due to data privacy concerns. To tackle this issue, we propose privacy-aware approach for detection that utilizes asynchronous federated convolutional neural networks (CNNs). Our method optimizes communication rounds by dividing CNN layers into shallow deep layers, being updated more frequently. In order enhance accuracy convergence central model, introduce temporally weighted aggregation takes advantage previously trained local models. evaluated on dataset, results show outperforms existing methods terms cost. Specifically, our achieves higher rate while requiring fewer rounds. suggest proposed promising solution improving also addressing concerns settings.

Language: Английский

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Personalization of Learning: Machine Learning Models for Adapting Educational Content to Individual Learning Styles

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 121114 - 121130

Published: Jan. 1, 2024

Language: Английский

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PIRAP: Medical Cancer Rehabilitation Healthcare Center Data Maintenance Based on IoT-Based Deep Federated Collaborative Learning

International Journal of Cooperative Information Systems, Journal Year: 2023, Volume and Issue: 33(01)

Published: April 11, 2023

Medical cancer rehabilitation healthcare center data maintenance is a global challenge with increased mortality risk. The Internet of Things (IoT)-based applications in were implemented through sensors and various connecting devices. main problem this procedure the privacy data, which biggest IoT, as all connected devices transfer real time, integration multiple other protocols can be hacked by end-to-end connection, it not secure, security issues may crop up due to handling such massive time. Recent studies showed that more structured risk assessment needed secure medical maintenance. In respect, collaborative learning frameworks, Deep Federated Collaborative Learning (DFCL), are for study based on IoT-based systems proposed smart short-term Bayesian convolution network analysis. This DFCL approach has been preferred context, strengthening allowing sensitive retained. Experiments benchmark datasets demonstrate federated model balances fairness, privacy, accuracy. paper, we analyze administrative count stages taken from 2016 2022, include routine operations. It frequently used assess achieving an accuracy range 19.8%. leading diagnoses per patient’s cost stay identifying disease, illness, or examining unusual combination symptoms made accurate diagnosis 26% efficient than diagnosis. hospital dictionary analysis visualization summary; 50% higher existing summary. By comparing dictionary, home health care shows 44.5% rate patient Moreover, adult day-care centers analyzed 88.6% 750 patients.

Language: Английский

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A Comprehensive Survey on Federated Learning in the Healthcare Area: Concept and Applications

Computer Modeling in Engineering & Sciences, Journal Year: 2024, Volume and Issue: 140(3), P. 2239 - 2274

Published: Jan. 1, 2024

Federated learning is an innovative machine technique that deals with centralized data storage issues while maintaining privacy and security.It involves constructing models using datasets spread across several centers, including medical facilities, clinical research Internet of Things devices, even mobile devices.The main goal federated to improve robust benefit from the collective knowledge these disparate without centralizing sensitive information, reducing risk loss, breaches, or exposure.The application in healthcare industry holds significant promise due wealth generated various sources, such as patient records, imaging, wearable surveys.This conducts a systematic evaluation highlights essential for selection implementation approaches healthcare.It evaluates effectiveness strategies field offers analysis domain, encompassing metrics employed.In addition, this study increasing interest applications among scholars provides foundations further studies.

Language: Английский

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Clustering-based binary Grey Wolf Optimisation model with 6LDCNNet for prediction of heart disease using patient data

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Jan. 8, 2025

In recent years, the healthcare data system has expanded rapidly, allowing for identification of important health trends and facilitating targeted preventative care. Heart disease remains a leading cause death in developed countries, often to consequential outcomes such as dementia, which can be mitigated through early detection treatment cardiovascular issues. Continued research into preventing strokes heart attacks is crucial. Utilizing wealth related cardiac ailments, two-stage medical classification prediction model proposed this study. Initially, Binary Grey Wolf Optimization (BGWO) used cluster features, with grouped information then utilized input model. An innovative 6-layered deep convolutional neural network (6LDCNNet) designed conditions. Hyper-parameter tuning 6LDCNNet achieved an improved optimization method. The resulting demonstrates promising performance on both Cleveland dataset, achieving convergence 96% assessing severity, echocardiography imaging impressive 98% convergence. This approach potential aid physicians diagnosing severity diseases, interventions that significantly reduce mortality associated

Language: Английский

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