Hospital Selection Support System from Heterogeneous Data Source

Medical institutions keep accumulating medical data, which is highly complex. Government agencies have been working hard to utilize such complex and diverse types of medical data to diagnose patients diseases correctly and offer them the right treatment. Medical data comes from different sources, and most of it is unstructured. Big data computing is a new trend for future computing with a large-scale data set and can be divided into two paradigms: Batch-oriented computing and Real-time oriented computing (or stream computing). Batch computing is in general efficient in processing high volume data. The data are collected, stored, and processed in batches to produce the results. Big data analytics is often complex process of examining large and varied data sets to uncover information including hidden patterns, unknown correlations, market trends and customer preferences that can help organizations make informed business decisions. Apache Hadoop is an example of batch-oriented computing. In this paper we are introducing a system which analyses the huge amount of clinical data using Hadoop and Hive to find the number of hospitals in an area, the best hospital in a particular area, the best hospital for a particular disease/problem etc. The analysis is done based on the users i. e. the patients point of view. The resultant data is presented to the users through a web application which helps the users in deciding a hospital based on their disease and geographical location and fixing appointment with the particular hospital/doctor through the web application online.