Prediction of Accuracy in Emergency Health Records using Hybrid Machine Learning Model
G. S. Raghavendra *
Atria Institute of Technology, Bangalore, Karnataka, India and RVR & JC College of Engineering, Guntur, Andhra Pradesh, India.
Shanthi Mahesh
Atria Institute of Technology, Bangalore, Karnataka, India and RVR & JC College of Engineering, Guntur, Andhra Pradesh, India.
M. V. P. Chandra Sekhara Rao
Atria Institute of Technology, Bangalore, Karnataka, India and RVR & JC College of Engineering, Guntur, Andhra Pradesh, India.
*Author to whom correspondence should be addressed.
Abstract
The quantity of digital information contained in electronic health records(EHR) has increased dramatically during the last ten years. Numerous researchers have discovered that these records may be used for a variety of other purposes as well, including applications in clinical informatics. Additionally, within the same time period, significant advancements in the area of deep learning have been made by the machine learning community. Using EHR data, we examine the existing research on applying deep learning to clinical activities. In this article we will discuss various deep learning techniques used for the classification of electronic health records along with proposing of Hybrid model for finding classification accuracy of various models.
Keywords: MHR, CNN, botlzmann machine, hybrid model, naïve bayes