2022
DOI: 10.4018/ijrqeh.299959
|View full text |Cite
|
Sign up to set email alerts
|

Prediction of Diabetic Retinopathy Using Health Records With Machine Learning Classifiers and Data Science

Abstract: Diabetes is a rapidly spreading disease. When the pancreas produces insufficient insulin or the body cannot utilise it effectively. Diabetic Retinopathy (DR) and blindness are two major issues for diabetics. Diabetes patients increase the amount of data collected about DR. To extract important information and undiscovered knowledge from data, data mining techniques are required. DM is necessary in DR to improve society's health. Our study focuses on the early detection of Diabetic Retinopathy using patient inf… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
2
1

Citation Types

0
5
0

Year Published

2022
2022
2024
2024

Publication Types

Select...
6
2
2

Relationship

2
8

Authors

Journals

citations
Cited by 15 publications
(5 citation statements)
references
References 22 publications
0
5
0
Order By: Relevance
“…Although the above models have achieved improvement in the accuracy of summary generation, the recurrent neural network and its variants are all time-step-based sequence structures, which seriously hinders the parallel training of the model [16][17][18], resulting in the inference process being limited by memory, resulting in reduced encoding and decoding speed of the summary generation model, and increased training overhead [19][20][21][22][23]. On the other hand, the above works optimize the model to maximize the ROUGE index or maximum likelihood without considering the coherence or fluency of the summary sentence [24][25][26] and relying on the ground-truth value of the annotated summary text in advance. With supervised training, the data cost involved in model training is high.…”
Section: Related Workmentioning
confidence: 99%
“…Although the above models have achieved improvement in the accuracy of summary generation, the recurrent neural network and its variants are all time-step-based sequence structures, which seriously hinders the parallel training of the model [16][17][18], resulting in the inference process being limited by memory, resulting in reduced encoding and decoding speed of the summary generation model, and increased training overhead [19][20][21][22][23]. On the other hand, the above works optimize the model to maximize the ROUGE index or maximum likelihood without considering the coherence or fluency of the summary sentence [24][25][26] and relying on the ground-truth value of the annotated summary text in advance. With supervised training, the data cost involved in model training is high.…”
Section: Related Workmentioning
confidence: 99%
“…The First, the author provided an arrival-service model for FoT data traffic based on multilayer waiting for lines with finite-size intervals. The proposed policy was compared to first-in-first-out and multi-priority-discipline queue strategies with the help of a complete study of wait times and gaps in wait times [23][24][25]. Machine learning and Clustering based various methods are used for the text analysis [26], internet of things [27][28][29][30][31][32][33], and disease detection [34].…”
Section: Related Workmentioning
confidence: 99%
“…Invasive malignancies cannot heal quickly and might spread to other regions of the body. ese are deadly malignancies that can sprawl to other organs of the human body which results in metastatic cancer [6][7][8][9].…”
Section: Breast Cancermentioning
confidence: 99%