Application of Logistic Regression Methods to Retinal Damage Detection on Digital Fundus Images

Authors

  • Umniy Salamah  Faculty of Computer Science, Universitas Mercu Buana, Jakarta Barat, Indonesia

DOI:

https://doi.org//10.32628/CSEIT206217

Keywords:

Digital Fundus Images, Machine Learning, Logistic Regression

Abstract

The predictions about the number of people with diabetes will be increased which leads to a reduced balanced ratio between the quality of the eye care service providers with the number of patients. The alternative to solve this problem is to provide early detection service for the last condition of eye health in the diabetic patients. To detect the damage of the retina can be done help machine learning algorithm of the logistics regression. The justification for selection the logistic regression algorithm for retina damage detection in this research is that it has been widely used in a variety of machine learning problems where LR can describe the response variables with one or more variables predictors well. The methodology of research contained five phases, including preparation, feature extraction, normalization, classification, evaluation for processing dataset of digital fundus image were provided by EyePACS using scikit-learn as machine learning library and the Python as programming language. As result, we found the accuracy of retina damage detection using logistic regression is 0.7392 with following by F1-score 0.6317, Recall 0.7392, Precision 0.6043 and Kappa 0.0051.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2020-07-30

Issue

Volume 6, Issue 4, July-August-2020

Section

Research Articles

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How to Cite

[1]
Umniy Salamah, " Application of Logistic Regression Methods to Retinal Damage Detection on Digital Fundus Images, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 6, Issue 4, pp.103-109, July-August-2020. Available at doi : https://doi.org/10.32628/CSEIT206217