Predicting the High-risk Patient using Virtual Physiological Human Technique in Big Data Health Care.

A. Jameer Basha; S Suguna; M Kowsigan; A Suresh Kumar; V Roopa

Author(s)

A. Jameer Basha Department of Information Technology, Sri Krishna College of Technology, Coimbatore, India

S Suguna Department of Information Technology, Sri Krishna College of Technology, Coimbatore - 641042, India

M Kowsigan Department of Information Technology, Sri Krishna College of Technology, Coimbatore - 641042, India

A Suresh Kumar Department of Information Technology, Sri Krishna College of Technology, Coimbatore - 641042, India

V Roopa Department of Information Technology, Sri Krishna College of Technology, Coimbatore - 641042, India

Abstract

Big data plays a key function in all aspects of organization especially in health science and clinical information. In the past days every health care related data contains data in the format of hard copy. (i.e.) Drug Prescription, consultants report, radiographs, laboratory prescription etc. But, nowadays with the increasing need for ample variety of clinical data access over the internet, every data is move towards cloud in the outward appearance of digitization. The immense quantity of information collected from the various health cares, hospitals, and physicians etc. which are stored remotely on the cloud on top of the internet is called big data. This big data is in the appearance of structured (Tables in the outward appearance of rows and columns), semi- structured (XML data) and unstructured data (videos, X-rays, scan report, Images, Audios etc.). This enormous size of information needs to store, processed and retrieved in the effective manner to provide accurate and valuable information to the doctors, patients, house surgeon and medical researchers. This manuscript is mainly focused to predict and analyze the symptoms and also categorize the high-risk patients. Here the association rule is applied in the VPH (Virtual Physiological Human) method. VPH technique is used for collecting, analyzing, observing, categorizing and indexing the data which are observed from various hospitals, Patient record, primary health care, Insurance Firms etc. Here the experts from various fields are analyze and verify the data. Associate rule is used for uncovering the association among the symptoms in the database. From the data collected, we apply Apriori algorithm to the data base and also gives the risk priority category and highest risk patient.

Publication Details

Page(s) 635-642

DOI DOI not available

Published Journal: Pakistan Journal of Biotechnology, Volume: 14, Issue: 4, Year: 2017

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