Open Access Journal

ISSN : 2394-2320 (Online)

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

Open Access Journal

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

ISSN : 2394-2320 (Online)

Call For Paper : Vol 11, Issue 03, March 2024

Call for Paper

Vol 11, Issue 03, March 2024

Indexing

Machine Learning Methods for Medical Diagnosis VIA Web Application

Author : Anz Joseph 1 Ashik Mohamed K 2 Venvas Emmanuvel A 3 Mrs. Paul T Sheeba 4

PDF

Date of Publication :7th April 2016

Abstract: In this era of medical field ,this particular industry is developing in a rapid speed . Open information and data explosion in healthcare industry is on a tipping point. Big Data plays a major role in this new change. One of the biggest challenges that the medical industry faces while it steps up digitization is the disparate data, speed of generation of this data and complexity arising out of multiple & non-standard formats. Patient data residing in disparate systems is a roadblock to having the right information at the right time. Clinical Decision Support systems need a single view of the patient for making better diagnosis and treatments. Patient identification and matching is a critical challenge in interfacing to the Electronic Health Record (EHR). Different documents and results from various disparate systems like laboratory, pharmacy, claims systems etc. need to be linked to the correct patient record. At this point when healthcare organizations share patient information internally as well as externally, patient records from numerous disparate databases should be connected effectively to guarantee that the decisions made by the clinicians are based on correct patient records and minimizing duplicate information and overheads. This will help to do better diagnosis process. This paper attempts to study the problem disparate systems and proposes a solution by using a social network for medical care and Data mining techniques for better clinical decision support and diagnosis. The main benefits of the proposed system are scalability, cost-effectiveness, flexibility of using and handling of any data source and ease in medical diagnosis.

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