Data Fusion Approach for Managing Clinical Data in an Industrial Environment using IoT

As health issues continue to become more prevalent as the population grows, building a public health network is critical for enhancing the overall health quality of the community. This study offers an Internet of Things (IoT) based health care system that can be employed in the context of community...

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Bibliographic Details
Published in:Scientific programming 2022-05, Vol.2022, p.1-10
Main Authors: Kulkarni, Mrunalini Harish, Kulkarni, Chaitanya, Babu, K. Suresh, Rahin, Saima Ahmed, Singh, Shweta, Kumar, D. Dinesh
Format: Article
Language:English
Subjects:
Abnormalities
Algorithms
Back propagation networks
Cardiovascular disease
Chronic illnesses
Cluster analysis
Data integration
Health services
Industrial areas
Internet of Things
Medical electronics
Medical research
Optimization
Prediction models
Principal components analysis
Public health
Structural health monitoring
Support vector machines
Swarm intelligence
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Summary:As health issues continue to become more prevalent as the population grows, building a public health network is critical for enhancing the overall health quality of the community. This study offers an Internet of Things (IoT) based health care system that can be employed in the context of community medical care industrial areas. The main focus of this research is to develop a disease prediction strategy that could be applied to community health services using theoretical modelling. Using principal component analysis (PCA) and cluster analysis, an artificial bee colony (ABC) creates a nonlinear support vector machine (SVM) classifier pair. Feature-level fusion analysis was performed to detect probable abnormalities. The results of the experiments reveal that the SVM model offers significant benefits in disease prediction. In the SVM illness prediction model, the ABC algorithm has the best parameter optimization effect in terms of accuracy, time, and other factors. The suggested method outperformed the traditional SVM and BP neural network methods by 17.24 percent and 72.41 percent, respectively. It can lower the RMSE and improve assessment indicators like the precision recall rate and the F-measure, demonstrating the method’s validity and accuracy. As a result, it is frequently used in community health management, geriatric community monitoring, and clinical medical therapy in an industrial environment.
ISSN:1058-9244
1875-919X
DOI:10.1155/2022/3603238