Exploratory Data Analysis for Social Big Data Using Regression and Recurrent Neural Networks

Abstract

In general the health care system and hospital takes a major role in the service sector. The clinical treatment successfully increases in the year for the treatment of both the medical and the technical innovations. A sample theoretical challenge exists in the patient flow analysis for real data. An existing method target on the audience and less concentration on the secondary statistical analysis, where the data obtain from the hospital not suitable for the analysis. So this limitation can overcome using the Exploratory Data Analysis (EDA), which helps in analysis of the patients flow in large hospital. The proposed frame work uses a machine learning method for the data classification processes. The feature extraction processes for the patient data applied for the larger hospital dataset and the individual hospital data. Some similar features are allowed to train over the Recurrent Neural Network (RNN) classifier for data modeling using the large hospital dataset. The output of the classifier has the specific details about the patients taken for the EDA method. The linear regression algorithm be the one kind of statistical tool for predicting the relationship between the variables. The proposed frame work is implemented using Mat lab R2014a software and the results were simulated. The relationship between the patient details and hospital information shows the status of the hospital as healthy and un healthy status.