Search

Smart healthcare systems in the cloud-IoT framework are designed for the prediction of heart disease. This smart diagnosis improves the patient's health status and minimizes the death rate. Early prediction of heart disease may reduce the risk of patient illness and monitoring in real-time to avoid the risk. The view of existing algorithms is inaccurate in early prediction which took a lot of time for the prediction and inaccurate early prediction of heart disease. To overcome these issues, this paper proposed a sparse autoencoder with Galactic Swarm Optimization (SAE-GSO) algorithm. A sparse encoder predicts heart disease and enhances the accurate prediction, tuning the parameters of sparsity regularity in the sparse autoencoder, Galactic Swarm optimization algorithm is implemented. The proposed work enhances the prediction rate of heart diseases, minimizing the error rate, and maximizing the accuracy. The accuracy rate of the proposed work of SAE-GSO in the Cleveland Dataset produces got 92.23 %, GBT got 65.12 %, SAE got 87.34%, and NB got 83.16 %. The accuracy rate of the proposed work of SAEGSO in the Framingham Dataset produced 92.59 %, GBT got 69.16 %, SAE got 86.25%, and NB got 82.37%. [ABSTRACT FROM AUTHOR]