Application of machine learning algorithms on diabetic retinopathy

Diabatic Retinopathy (DR) is one of the leading cause of sight inefficiency for diabetic patients. The clinical diagnostic results and several outcome of eye testing methods reviled a set of observations that eases the decision making in the case of diabetic retinopathy for the doctor, therapist. Ma...

Full description

Saved in:
Bibliographic Details
Published in2017 2nd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT) pp. 2046 - 2051
Main Authors Pal, Ridam, Poray, Jayanta, Sen, Mainak
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2017
Subjects
Diabetes
Diabetic retinopathy
Machine learning algorithms
Machine learning algorithms in medical diagnostics
Python in machine learning algorithms
Retina
Retinopathy
Support vector machine
Support vector machines
Training
WEKA simulator
Online AccessGet full text

Cover

More Information
Summary:Diabatic Retinopathy (DR) is one of the leading cause of sight inefficiency for diabetic patients. The clinical diagnostic results and several outcome of eye testing methods reviled a set of observations that eases the decision making in the case of diabetic retinopathy for the doctor, therapist. Machine learning, a branch of artificial intelligence is applied in clinical data analytic as it can detect patterns in data, and then use these uncovered patterns to predict future data or perform some kind of decision making under uncertainty. In case of DR finding the co-relation between the depth of affection and the clinical result is very much critical, as several parameters are need to be taken into consideration for optimal decision making by the therapist. In this paper we have reviewed the performance of a set of machine learning algorithms and verify their performance for a particular DR data set.
DOI:10.1109/RTEICT.2017.8256959