Real time health care big data analytics model for improved QoS in cardiac disease prediction with IoT devices

Safa, M.; Pandian, A.; Gururaj, H L; Ravi, Vinayakumar; Krichen, Moez

doi:10.1007/s12553-023-00747-1

Cited by 26 publications

(16 citation statements)

References 42 publications

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Section: Methodsmentioning

confidence: 99%

“…It is unquestionably constrained enough to prevent it from being able to retain all of the data from each stream, though. Let us take a moment to consider how frequently stream data occurs before going on [42]. A situation where a sensor, originally measuring the ocean's surface temperature, develops into a more complex instrument with the inclusion of a GPS unit in the world of healthcare and the Internet of Medical Things (IoMT).…”

Section: Proposed Model For Data Gathering Techniquementioning

confidence: 99%

See 1 more Smart Citation

Semi-Supervised Clustering-Based DANA Algorithm for Data Gathering and Disease Detection in Healthcare Wireless Sensor Networks (WSN)

Sinha,

Aljrees,

Pandey

et al. 2023

Sensors

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Wireless sensor networks (WSNs) have emerged as a promising technology in healthcare, enabling continuous patient monitoring and early disease detection. This study introduces an innovative approach to WSN data collection tailored for disease detection through signal processing in healthcare scenarios. The proposed strategy leverages the DANA (data aggregation using neighborhood analysis) algorithm and a semi-supervised clustering-based model to enhance the precision and effectiveness of data collection in healthcare WSNs. The DANA algorithm optimizes energy consumption and prolongs sensor node lifetimes by dynamically adjusting communication routes based on the network’s real-time conditions. Additionally, the semi-supervised clustering model utilizes both labeled and unlabeled data to create a more robust and adaptable clustering technique. Through extensive simulations and practical deployments, our experimental assessments demonstrate the remarkable efficacy of the proposed method and model. We conducted a comparative analysis of data collection efficiency, energy utilization, and disease detection accuracy against conventional techniques, revealing significant improvements in data quality, energy efficiency, and rapid disease diagnosis. This combined approach of the DANA algorithm and the semi-supervised clustering-based model offers healthcare WSNs a compelling solution to enhance responsiveness and reliability in disease diagnosis through signal processing. This research contributes to the advancement of healthcare monitoring systems by offering a promising avenue for early diagnosis and improved patient care, ultimately transforming the landscape of healthcare through enhanced signal processing capabilities.

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Section: Methodsmentioning

confidence: 99%

Section: Proposed Model For Data Gathering Techniquementioning

confidence: 99%

Semi-Supervised Clustering-Based DANA Algorithm for Data Gathering and Disease Detection in Healthcare Wireless Sensor Networks (WSN)

Sinha,

Aljrees,

Pandey

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…Out of all, the RF performs better with 98.7% accuracy. The comparison of our proposed architecture with the existing works [2,16,19,22,[31][32][33][34][35][36][37][38][39][40][41] are summarized in Table 1. In addition, the summary of previous works [40][41][42][43][44][45][46][47] related to intelligent cardiac data analysis is presented in Table 2.…”

Section: Related Workmentioning

confidence: 99%

Healthcare Big Data Analysis with Artificial Neural Network for Cardiac Disease Prediction

Mohapatra,

Sahoo,

Mohapatra

2023

Electronics

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The generation of a huge volume of structured, semi-structured and unstructured real-time health monitoring data and its storage in the form of electronic health records (EHRs) need to be processed and analyzed intelligently to provide timely healthcare. A big data analytic platform is an alternative to the traditional warehouse paradigms for the processing, analysis and storage of the tremendous volume of healthcare data. However, the manual analysis of these voluminous, multi-variate patients data is tedious and error-prone. Therefore, an intelligent solution method is highly essential to perform multiple correlation analyses for disease diagnosis and prediction. In this paper, first, a structural framework is proposed to process the huge volume of cardiological big data generated from the hospital and patients. Then, an intelligent analytical model for the cardiological big data analysis is proposed by combining the concept of artificial neural network (ANN) and particle swarm optimization (PSO) to predict the abnormalities in the cardiac health of a person. In the proposed cardiac disease prediction model, an extensive electrocardiogram (ECG) data analysis method is developed to identify the probable normal and abnormal cardiac feature points. Simulation results show the effects of a number of attributes for improving the accuracy of the cardiac disease prediction and data processing time in the cloud with an increase in the number of the cardiac patients.

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“…An example is the mentioned platform for the University of California (UC) San Diego Health System, which implemented a predictive analytics algorithm right into regular healthcare workflow. They take and analyze electronic health record (EHR) data and use deep learning (DL) models for the early detection of cases such as sepsis [12].…”

Section: Introductionmentioning

confidence: 99%

Unveiling the Influence of AI Predictive Analytics on Patient Outcomes: A Comprehensive Narrative Review

Dixon,

Sattar,

Moros

et al. 2024

Cureus

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This comprehensive literature review explores the transformative impact of artificial intelligence (AI) predictive analytics on healthcare, particularly in improving patient outcomes regarding disease progression, treatment response, and recovery rates. AI, encompassing capabilities such as learning, problem-solving, and decision-making, is leveraged to predict disease progression, optimize treatment plans, and enhance recovery rates through the analysis of vast datasets, including electronic health records (EHRs), imaging, and genetic data. The utilization of machine learning (ML) and deep learning (DL) techniques in predictive analytics enables personalized medicine by facilitating the early detection of conditions, precision in drug discovery, and the tailoring of treatment to individual patient profiles. Ethical considerations, including data privacy, bias, and accountability, emerge as vital in the responsible implementation of AI in healthcare. The findings underscore the potential of AI predictive analytics in revolutionizing clinical decision-making and healthcare delivery, emphasizing the necessity of ethical guidelines and continuous model validation to ensure its safe and effective use in augmenting human judgment in medical practice.

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Real time health care big data analytics model for improved QoS in cardiac disease prediction with IoT devices

Cited by 26 publications

References 42 publications

Semi-Supervised Clustering-Based DANA Algorithm for Data Gathering and Disease Detection in Healthcare Wireless Sensor Networks (WSN)

Semi-Supervised Clustering-Based DANA Algorithm for Data Gathering and Disease Detection in Healthcare Wireless Sensor Networks (WSN)

Healthcare Big Data Analysis with Artificial Neural Network for Cardiac Disease Prediction

Unveiling the Influence of AI Predictive Analytics on Patient Outcomes: A Comprehensive Narrative Review

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