Big Data-Based Optimized Deep Learning Model for Improving Performance of Electronics Health Care Data

Abstract

Bigdata analytics is a new area of supervised analytics that healthcare analytics has moved into. Healthcare data is transmissive, incremental, and substantial, and it has pre-set thresholds for classifying patient conditions and diseases. The necessary understanding of disease occurrences is applied to the analysis of this data. The research project introduces healthcare data analytics with a novel framework that leverages unstructured data for the classification of healthy and unhealthy samples in order to understand the nature of healthy and unhealthy labelled classes. This is motivated by the fact that healthcare data is informative and voluminous in nature. Additionally, the unknown samples' health status is predicted using the supervised knowledge. Each model presented in this work is supported by accuracy and a computational complexity model.