FEATURE EXTRACTION FOR DISEASE PREDICTION USING MACHINE LEARNING TECHNIQUES
Author(s):
D.VISHNU VARDHAN RAJU, DR. M.HUMERA KHANAM, A.KHUDHUS
Keywords:
BigdataAnalytics, Machine Learning,Health Care.
Abstract
Feature selection is an important technique for data mining. Despite its importance, most studies of feature selection are restricted to batch learning. Unlike traditional batch learning methods, online learning represents a promising family of efficientand scalable machine learning algorithms for large-scale applications.The machine learning field has gained its momentum in almost any domain of research and just recently has become a reliable tool in the medical domain .Machine Learning(ML) is envisioned as a tool by which computer-based systems can be integrated in the health care field in order to get more efficient medical care.An ML-based methodology is described to build an application that is capable of identifying disseminating health care information.It extracts sentences from published medical papers that mention diseases and treatments, and identifies semantic relations that exist between diseases and treatments.In this paper we have focused onMachine Learning (ML) techniques and the Classification algorithms that are suitable to use for identifying and classifying relevant medical information in short texts.The encouraging results of our experiments validate the efficiency of the proposed techniques.
Article Details
Unique Paper ID: 145189

Publication Volume & Issue: Volume 4, Issue 7

Page(s): 702 - 706
Article Preview & Download


Share This Article

Join our RMS

Conference Alert

NCSEM 2024

National Conference on Sustainable Engineering and Management - 2024

Last Date: 15th March 2024

Call For Paper

Volume 10 Issue 10

Last Date for paper submitting for March Issue is 25 June 2024

About Us

IJIRT.org enables door in research by providing high quality research articles in open access market.

Send us any query related to your research on editor@ijirt.org

Social Media

Google Verified Reviews