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 |Pages: 16
DOI: 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.
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Introduction To Diabetic Retinopathy

Damage to the retina's blood vessels, which would be the thin membrane that lines the eye's back wall, is to blame for this condition. This layer sends these signals are sent to the brain by this layer, which senses light and interprets what is seen. Some of the symptoms are floaters, blurriness, and dark spots or areas in the visual field. Moderate and mild cases of diabetes do not necessitate immediate treatment, but they should be closely monitored and managed carefully. Advanced circumstances necessitate medical or surgical intervention. The main goal of this study is to look at the findings of multiple studies and the progression of e-participation in India, both rich and poor (Gulati & Telu, 2016). Medical image acquisition is outlined in this article. The authors discuss its most common methods of acquiring images and assessing their significant threats and challenges in image-guided surgery. Medical image reconstruction systems' accuracy, dependability, and efficiency were also discussed (Alam et al., 2018). Diagnosis symptoms include the following:

Shadows or flashes of light in your vision

  • I.

    Blurry vision

  • II.

    Fluctuating vision

  • III.

    Colour-blindness impairment

your vision is blurred or distorted

The following figure 1 illustrates the state of diabetic retinopathy types. Figure source Type 2 Diabetes Complications (myhealthexplained.com):

Figure 1.

Represented the variances between Normal retina and Diabetic Retinopathy.

IJRQEH.299959.f01

Through a complete eye examination, DR can be diagnosed. Diagnosis may include:

  • § Diabetic patients and healthy controls will be compared for retinal thickness and retinal microcirculation (Cankurtaran et al., 2020)

  • § Retinal detachment

  • § Visual insight measurements to determine the affected area

  • § Abnormalities in optic nerve

  • § Size of the pressure within the eye

  • § Refraction to determine if a new eyeglass prescription is needed

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