Inter-Collateral Diabetic Retinopathy Extraction and Evaluation Using Trained Datasets of Multiple Feature Set

K T Ilayarajaa, E Logashanmugam

doi:10.17762/turcomat.v12i6.4036

Abstract

Diabetic Retinopathy (DR) is caused due to un-mounted diabetic comorbidities. The patients suffer complete vision blindness if untreated or diagnosed on later stage. In this article, we propose a novel approach for early detection and prediction using trained datasets of multiple features. The process expansion is resultant of multiple stage attribute extraction via a series of inter-collateral parameters of diabetics. Typically, the proposed technique is designed and developed on a multi-value and multi-dimension datasets such as comorbidities history of patient encountered during diabetics. The proposed technique uses collateral attributes in evaluating retinopathy status and thereby validates the extracted DR under threshold value comparisons. The results are computed using HADOOP framework for recursive pattern and feature evaluation. The trial is processed on UCL digital library datasets with estimated performance of 98.7% with extraction and 92.34% with value True-Positive (TP) prediction.

Highlights

The diabetic retinopathy (DR) is a regular formation of extra-perianal growth in the inner region of human lens causing a permanent or complete blindness if untreated in early stages
The current scenario of diabetic retinopathy is relatively pushed on back-foot due to the break of Global pandemic SAR CoVID-19 in late December of 2019 reported from Wuhan province of China
A study is focused on the design and development of a technique dedicated for the feature extraction and evaluation of DR based on a predictive model

Summary

Introduction

The diabetic retinopathy (DR) is a regular formation of extra-perianal growth in the inner region of human lens causing a permanent or complete blindness if untreated in early stages. According to a study [1] 72.96 million cases is reported in India as per the WHO survey. This causes a major concern of research and development to address the growing demand of treatment and drug design for developing countries population. The current scenario of diabetic retinopathy is relatively pushed on back-foot due to the break of Global pandemic SAR CoVID-19 in late December of 2019 reported from Wuhan province of China. The concern of diabetic’s retinopathy and related illness caused due to diabetics is rising on an alarming rate. A study is focused on the design and development of a technique dedicated for the feature extraction and evaluation of DR based on a predictive model. The process of decision making is relatively incubated with an ideal condition of attribute interdependency extraction

Methods

Results

Conclusion