Convolution Denoising Regularized Auto Encoder Stacked Method for Coronary Acute Syndrome in Internet of Medical Things Platform

Abstract

Presently, Coronary Acute Syndrome (CAS) is a widespread and extreme heart disease which is considered as one of the major concerns for death in the world with long-term disability. For early intervention and care, the prediction of CAS clinical risk is more critical during analysis. A minimum number of manually selected dimensions of risk are used for current CAS risk assessment models and statistical variables are often dichotomized to optimize storage in the Internet of medical things platform(IoMT) on the Encoder layer during data analysis. This research develops a Convolution Denoising Regularized Auto Encoder Stacked Method (CDRAESM) to normalize CAS patient's medical risks from high volumes of patient records and the characteristics have been analyzed during prediction. In this research, a true medical dataset of 3,464 CAS samples is used for experimental analysis and numerical reliability has been analyzed in Area Characteristics Curve (ACC) with an Accuracy range of 96.77%. The results show that the current health risk prediction using CDRAESM achieves competitive concert than conventional models which prevails in practice. Further, the reconstructive learning strategy approach can extract informational risk from the CAS and the risk factors has been identified with existing knowledge of the clinical domain and include theories that might be confirmed by further medical research.