A machine learning model for improving healthcare services on cloud computing environment. (April 2018)

• Record Type: Journal Article
• Authors: Abdelaziz, Ahmed; Elhoseny, Mohamed; Salama, Ahmed S.; Riad, A.M.
• Is Part Of: Measurement. Volume 119(2018)
• Page Start: 117
• Page End: 128
• Publication Date: 2018-04
• Subjects: Cloud computing -- Health services -- Parallel particle swarm optimization -- Linear regression -- Neural network -- Chronic kidney disease; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2018.01.022
• Languages: English
• ISSNs: 0263-2241

Abstract

Abstract: Recently, cloud computing gained an important role in healthcare services (HCS) due to its ability to improve the HCS performance. However, the optimal selection of virtual machines (VMs) to process a medical request represents a big challenge. Optimal selection of VMs performs a significant enhancement of the performance through reducing the execution time of medical requests (tasks) coming from stakeholders (patients, doctors, etc.) and maximizing utilization of cloud resources. For that, this paper proposes a new model for HCS based on cloud environment using Parallel Particle Swarm Optimization (PPSO) to optimize the VMs selection. In addition, a new model for chronic kidney disease (CKD) diagnosis and prediction is proposed to measure the performance of our VMs model. The prediction model of CKD is implemented using two consecutive techniques, which are linear regression (LR) and neural network (NN). LR is used to determine critical factors that influence on CKD. NN is used to predict of CKD. The results show that, the proposed model outperforms the state-of-the art models in total execution time the rate of 50%. In addition, the system efficiency regarding real-time data retrieval is greatly improved by 5.2%. In addition, the accuracy of hybrid intelligent model in predicting of CKD is 97.8%. The proposed model is superior to most of the referred models in the related works by 64%.