These days, cloud systems and the Internet of Things (IoT) are extensively used in a variety of medical services. Instead of relying on the limited storage and processing power found in mobile equipment, the vast amount of data generated by IoT equipment in the medical industry may be analysed on a cloud system. In this research, an internet healthcare judgment supporting platform for predicting chronic kidney disease (CR) is presented as a means of providing effective healthcare care. The data collection, preparation, and categorization of medical information are the three processes that the proposed model goes through in order to predict CKD. To categorise the data examples into CKD and non-CKD, the logistic regression (LR) method is used. Additionally, the Adaptive Moment Estimation (Adam) & adapt training rate optimisation algorithms are used to fine-tune the LR's settings. With the use of a reference CKD dataset, the effectiveness of the newly proposed model is evaluated. The test results showed that the provided model's better properties on the used dataset.
Authors: Prasanna Kumar Lakineni, Rajesh Singh, Bhavana Mandaloju, Shilpi Singhal, Monika Dixit Bajpai, Mohit Tiwari
DOI: https://doi.org/10.1109/icacite57410.2023.10183151
Publish Year: 2023
