Splitfed-based Patient Severity Prediction and Utility Maximization in Industrial Healthcare 4.0

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.

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