Abstract
The healthcare industry has transitioned from traditional healthcare 1.0 to AI-powered healthcare 4.0. However, overall cost for patient treatment remains high and challenging to manage due to the absence of a centralized cost evaluation mechanism before hospital visits. Therefore, in this paper, we devise a cloud-based mechanism to calculate hospitals' star rating based on questionnaire with the application of Z-score and K∗clustering algorithm. To evaluate disease severity at cloud, splitfed technique is utilized in coordination with Wireless Body Area Network (WBAN). Finally, the cloud calculates provisional treatment costs and finds a preferable hospital with a low payable treatment cost and satisfactorily high rating for the patient via utility maximization in a cloud-based environment. Moreover, the effectiveness of the proposed polynomial algorithmic model is shown theoretically, experimentally, and comparing with other state-of-the-art methods on real-world data.
Recommended Citation
H. Singh et al., "Splitfed-Based Patient Severity Prediction And Utility Maximization In Industrial Healthcare 4.0," ACM International Conference Proceeding Series, pp. 388 - 393, Association for Computing Machinery (ACM), Jan 2024.
The definitive version is available at https://doi.org/10.1145/3631461.3631953
Department(s)
Computer Science
Keywords and Phrases
Clustering; Computing; Healthcare 4.0.; Z-score Splitfed
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Association for Computing Machinery, All rights reserved.
Publication Date
04 Jan 2024
