Kernel random forest with black hole optimization for heart diseases prediction using data fusion.

ABSTRACT: In recent years, the Internet of Things has played a dominant role in various real-time problems and given solutions via sensor signals. Monitoring the patient health status of Internet of Medical Things (IoMT) facilitates communication between wearable sensor devices and patients through a wireless network. Heart illness is one of the reasons for the increasing death rate in the world. Diagnosing the disease is done by the fusion of multi-sensor device signals. Much research has been done in predicting the disease and treating it correctly. However, the issues are accuracy, consumption time, and inefficiency. To overcome these issues, this paper proposed an efficient algorithm for fusing the multi-sensor signals from wearable sensor devices, classifying the medical signal data and predict

SUBMITTER: Alluhaidan AS 

PROVIDER: S-EPMC11622926 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature