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

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

B. Sumathy, Arindam Chakrabarty, Sandeep Gupta, Sanil S. Hishan, Bhavana Raj, Kamal Gulati, Gaurav Dhiman
Copyright: © 2022 |Volume: 11 |Issue: 2 |Pages: 16
ISSN: 2160-9551|EISSN: 2160-956X|EISBN13: 9781683182580|DOI: 10.4018/IJRQEH.299959
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MLA

Sumathy, B., et al. "Prediction of Diabetic Retinopathy Using Health Records With Machine Learning Classifiers and Data Science." IJRQEH vol.11, no.2 2022: pp.1-16. http://doi.org/10.4018/IJRQEH.299959

APA

Sumathy, B., Chakrabarty, A., Gupta, S., Hishan, S. S., Raj, B., Gulati, K., & Dhiman, G. (2022). Prediction of Diabetic Retinopathy Using Health Records With Machine Learning Classifiers and Data Science. International Journal of Reliable and Quality E-Healthcare (IJRQEH), 11(2), 1-16. http://doi.org/10.4018/IJRQEH.299959

Chicago

Sumathy, B., et al. "Prediction of Diabetic Retinopathy Using Health Records With Machine Learning Classifiers and Data Science," International Journal of Reliable and Quality E-Healthcare (IJRQEH) 11, no.2: 1-16. http://doi.org/10.4018/IJRQEH.299959

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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 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. Our 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, our research suggests that bagged trees and KNN are good classifiers for DR.