Improving the Cloud Healthcare Services by Introducing AI for Better Performance and Quality

Cloud computing has gained widespread popularity in various fields, especially in healthcare field where the cloud provide medical services without having to own or maintain the medical devices. This research offers an effective system to ensure the availability of cloud healthcare services by detecting faults in the cloud system to ensure the availability of resources, and making accurate predictions over a short period of time to improve the quality of service and to avoid waste and improve security and privacy. The methodology has been applied by using OpenStack platform for fault injection and Google Colab for data preprocessing. A variety of machine learning algorithms have been used to improve the accuracy of prediction: the Logistic Regression, Support Vector Classification, Random Forest, k-Nearest Neighbors and decision Tree. The comparison results showed that the use of the SVC classification gave the best accuracy to predict faults, reaching 98%.