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Prediction of diabetic retinopathy using health records with machine learning classifiers and data Science

Diabetes is a rapidly spreading disease. It occurs 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 impor...

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Main Authors: Sumathy, B., Chakrabarty, Arindam, Gupta, Sandeep, Hishan, Sanil S., Raj, Bhavana, Gulati, Kamal, Dhiman, Gaurav
Format: Article
Language:English
Published: IGI Global 2022
Subjects:
HB615-715 Entrepreneurship. Risk and uncertainty. Property
Online Access:http://eprints.utm.my/id/eprint/101400/1/SanilSHishan2022_PredictionofDiabeticRetinopathyUsingHealthRecords.pdf
http://eprints.utm.my/id/eprint/101400/
http://dx.doi.org/10.4018/IJRQEH.299959
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spelling my.utm.1014002023-06-14T10:02:38Z http://eprints.utm.my/id/eprint/101400/ Prediction of diabetic retinopathy using health records with machine learning classifiers and data Science Sumathy, B. Chakrabarty, Arindam Gupta, Sandeep Hishan, Sanil S. Raj, Bhavana Gulati, Kamal Dhiman, Gaurav HB615-715 Entrepreneurship. Risk and uncertainty. Property Diabetes is a rapidly spreading disease. It occurs 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. The study focuses on the early detection of diabetic retinopathy using patient information. DM approaches are used to extract information from these numeric records. The dataset was used to forecast DR using logistic regression, KNN, SVM, bagged tree, and boosted tree classifiers. Two cross-validations are used to find the best features and avoid overfitting. The dataset includes 900 diabetes patients. The boosted tree produced the best classification accuracy (90.1%) with 10% hold-out validation. KNN also achieved 88.9% accuracy, which is impressive. As a result, the research suggests that bagged trees and KNN are good classifiers for DR. IGI Global 2022-04 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/101400/1/SanilSHishan2022_PredictionofDiabeticRetinopathyUsingHealthRecords.pdf Sumathy, B. and Chakrabarty, Arindam and Gupta, Sandeep and Hishan, Sanil S. and Raj, Bhavana and Gulati, Kamal and Dhiman, Gaurav (2022) Prediction of diabetic retinopathy using health records with machine learning classifiers and data Science. International Journal of Reliable and Quality E-Healthcare, 11 (2). n/a. ISSN 2160-9551 http://dx.doi.org/10.4018/IJRQEH.299959 DOI: 10.4018/IJRQEH.299959
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic HB615-715 Entrepreneurship. Risk and uncertainty. Property
spellingShingle HB615-715 Entrepreneurship. Risk and uncertainty. Property
Sumathy, B.
Chakrabarty, Arindam
Gupta, Sandeep
Hishan, Sanil S.
Raj, Bhavana
Gulati, Kamal
Dhiman, Gaurav
Prediction of diabetic retinopathy using health records with machine learning classifiers and data Science
description Diabetes is a rapidly spreading disease. It occurs 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. The study focuses on the early detection of diabetic retinopathy using patient information. DM approaches are used to extract information from these numeric records. The dataset was used to forecast DR using logistic regression, KNN, SVM, bagged tree, and boosted tree classifiers. Two cross-validations are used to find the best features and avoid overfitting. The dataset includes 900 diabetes patients. The boosted tree produced the best classification accuracy (90.1%) with 10% hold-out validation. KNN also achieved 88.9% accuracy, which is impressive. As a result, the research suggests that bagged trees and KNN are good classifiers for DR.
format Article
author Sumathy, B.
Chakrabarty, Arindam
Gupta, Sandeep
Hishan, Sanil S.
Raj, Bhavana
Gulati, Kamal
Dhiman, Gaurav
author_facet Sumathy, B.
Chakrabarty, Arindam
Gupta, Sandeep
Hishan, Sanil S.
Raj, Bhavana
Gulati, Kamal
Dhiman, Gaurav
author_sort Sumathy, B.
title Prediction of diabetic retinopathy using health records with machine learning classifiers and data Science
title_short Prediction of diabetic retinopathy using health records with machine learning classifiers and data Science
title_full Prediction of diabetic retinopathy using health records with machine learning classifiers and data Science
title_fullStr Prediction of diabetic retinopathy using health records with machine learning classifiers and data Science
title_full_unstemmed Prediction of diabetic retinopathy using health records with machine learning classifiers and data Science
title_sort prediction of diabetic retinopathy using health records with machine learning classifiers and data science
publisher IGI Global
publishDate 2022
url http://eprints.utm.my/id/eprint/101400/1/SanilSHishan2022_PredictionofDiabeticRetinopathyUsingHealthRecords.pdf
http://eprints.utm.my/id/eprint/101400/
http://dx.doi.org/10.4018/IJRQEH.299959
_version_ 1769842049609105408
score 13.211869

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