DOI:10.20894/IJCOA.
Periodicity: Bi Annual.
Impact Factor:
SJIF:5.079 & GIF:0.416
Submission:Any Time
Publisher: IIR Groups
Language: English
Review Process:
Double Blinded

Paper Template
Copyright Form
Subscription Form
web counter
web counter

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

IJCOA provides online manuscript tracking system.

Every issue of Journal of IJCOA 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

IJCOA 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 IJCOA website. For complete procedure, contact us at admin@iirgroups.org

SCIA Journal Metrics


SCIA-GRAPH
SCIA-SAI
Published in:   Vol. 9 Issue 1 Date of Publication:   June 2020
Page(s):   45-50 Publisher:   Integrated Intelligent Research (IIR)
DOI:   10.20894/IJCOA.101.009.001.007 SAI :

The enormous growth of data in biomedical and healthcare communities need accurate analysis of medical data benefits in early disease detection, patient care and society services. However, the accuracy of analysis data will be condensed when the eminence of health care data is imperfect. In heath care community different regions exist with regional disease which would not be easily predicted with maximum of accuracy level. In this paper, we predict the diabetes disease and compare the algorithm which algorithm provide high performance, finally select the best algorithm to predict the diabetes disease at early stage. Machine learning algorithm can be applied for diabetes disease automating classification. This paper compares several Machine learning algorithms for classifying diabetes disease. Algorithms that involve Decision Tree, Naive Bayes, and KNN, SVM are proposed and assessed for this classification. These approaches have been tested with PIMA Indian Diabetes Dataset downloaded from UCI machine learning data repository. The performances of the algorithms have been compared in terms of Accuracy, Sensitivity, and Specificity with help of Sciklit-learn. Sciklit-learn are a free software machine learning library for the Python programming language. Finally comes with best suitable model for predict diabetes diseases.