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Title	Smart Iot_Cloud-Based Ml Technique To Classify Breast Cancer
ID_Doc	51104
Authors	Prusty S.; Dash S.K.; Prusty S.G.P.; Dalai S.S.; Tripathy N.; Nayak S.K.
Year	2023
Published	2023 2nd International Conference on Ambient Intelligence in Health Care, ICAIHC 2023
DOI	http://dx.doi.org/10.1109/ICAIHC59020.2023.10431475
Abstract	IoT and cloud computing have a significant impact on healthcare services, particularly when it comes to disease prediction in smart cities. Big data about breast cancer (BC) can be sent through IoT devices (digital sensors, etc.) and stored in the cloud. As a result, these huge data are used to improve BC prediction accuracy in a cloud setting. Thus, BC-based cloud IoT is thought to be a significant issue that smart city stakeholders in health care must deal with. In smart cities, cloud computing helps patients predict the presence of BC anywhere and at any time by using advanced machine learning (ML) techniques. The main goal of this study is to demonstrate the effectiveness of a secure Internet of Things and cloud computing in the identification of BC at our home. Thus, we proposed an ICM (IoT_Cloud-based ML) method that describes the workflow of how a user can predict BC disease at any place and at any time. And finally, we designed performance metrics for all three models and found logistic regression model (LR) outperforms with higher accuracy of 83% than DT at 72% and NB at 80%. However, all these experiments have been carried out using Python 3.8.3 programming software on the Jupyter 6.4.3 Notebook platform in Windows 10 operating system. © 2023 IEEE.
Author Keywords	breast cancer; IoT cloud computing; machine learning

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