K. J. Somaiya

The advent of intelligent systems that improve security, dependability, and efficiency has been made possible by recent developments in information technology, which have been driven by the creation of smart cities. In this regard, the healthcare industry has made use of these developments to enhance the caliber of healthcare services through creative methods of patient information management. Healthcare is no exception to the industries that have been transformed by the Internet of Things (IoT) and cloud computing. Intelligent and sustainable healthcare services have a huge potential to be enabled by the development of smart healthcare systems that make use of IoT devices and cloud infrastructure. The machine learning method described uses the cloud-centric IoT paradigm to improve the functionality of smart healthcare systems.In this paper, we suggest a framework for achieving intelligent and long-lasting healthcare solutions by fusing machine learning methods with cloud-centric IoT. These data are then safely transferred and kept in the cloud for additional examination and processing. Our proposed method use machine learning algorithms to analyze the gathered data and derive valuable insights, enabling smarter healthcare services. The massive amount of data available in the cloud is continuously used to train and update the machine learning models, allowing them to gradually increase their accuracy and performance. The incorporation of Internet of Things (IoT) devices in a cloud computing environment is crucial to developing sustainable computing solutions in e-healthcare applications. However, the energy required to send data from IoT devices to cloud servers is quite significant, making the use of clustering techniques to cut down on energy usage necessary.