DOI:10.20894/IJDMTA.
Periodicity: Bi Annual.
Impact Factor:
SJIF:4.893 & GIF:0.787
Submission:Any Time
Publisher: IIR Groups
Language: English
Review Process:
Double Blinded

News and Updates

Author can submit their paper through online submission. Click here

Paper Submission -> Blind Peer Review Process -> Acceptance -> Publication.

On an average time is 3 to 5 days from submission to first decision of manuscripts.

Double blind review and Plagiarism report ensure the originality

IJDMTA provides online manuscript tracking system.

Every issue of Journal of IJDMTA is available online from volume 1 issue 1 to the latest published issue with month and year.

Paper Submission:
Any Time
Review process:
One to Two week
Journal Publication:
June / December

IJDMTA special issue invites the papers from the NATIONAL CONFERENCE, INTERNATIONAL CONFERENCE, SEMINAR conducted by colleges, university, etc. The Group of paper will accept with some concession and will publish in IJDMTA website. For complete procedure, contact us at admin@iirgroups.org

Paper Template
Copyright Form
Subscription Form
web counter
web counter
Published in:   Vol. 3 Issue 2 Date of Publication:   December 2014

A Review Paper on Outlier Detection using Two-Phase SVM Classifiers with Cross Training Approach for Multi- Disease Diagnosis

Kalpit R. Chandpa,Jignasa N. Patel

Page(s):   55-59 ISSN:   2278-2397
DOI:   10.20894/IJDMTA.102.003.002.003 Publisher:   Integrated Intelligent Research (IIR)

Data mining techniques can be effectively used for major disease prediction and diagnosis. But we argue that diagnosis of some major diseases those are correlated with several small diseases and highly interrelated with each other such as TB and HIV and multi disease diagnosis is tough and challenging task. In Data mining, Outlier detection is useful technique for medical diagnosis. When availability of class labels is there, supervised outlier detection successfully used to detect rare and abnormal objects for disease diagnosis purpose. Classification techniques such as Nave Bayesian classifier, SVM, Association rule mining, neural networks are used for outlier detection. SVM is one of the best classification techniques for outlier detection as there is no requirement of explicit statistical model for SVM and it avoids dimensionality problem and provides optimum solution for classification. Outlier detection using SVM can be possible with One class SVM and Two-class SVM. But still One class SVM suffers with non availability of accurate class labels and Two-class SVM is suffer when one of the class is under sampled and long training time. This paper provides survey of disease diagnosis using SVM and Outlier detection using SVM on different domains and we have identified some key challenges in this field and introduced a solution of two-phase Outlier detection approach combining both Two-class SVM and One class SVM for prediction and diagnosis of multiple diseases those are highly interrelated with each other using available class labels of both data sets and proposed cross training approach for both SVM classifiers for reduce training time of SVM and accuracy purpose.