Diabetes risk prediction model of connected organs using retinal images

Medical services are the quickest developing space that expects to give the fitting therapy to postpone the sickness movement. Medical services space comprises of the most significant subdomain called sickness forecast which needs an effective procedure to anticipate the infection in beginning phase...

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Bibliographic Details
Main Authors: Gokulraj, J., Azath, H., Geetha, D., Ramkumar, M., Babu, T. R. Ganesh
Format: Conference Proceeding
Language:English
Subjects:
Decision trees
Diabetes
Health services
Illnesses
Mathematical models
Navy
Prediction models
Retinal images
Support vector machines
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Summary:Medical services are the quickest developing space that expects to give the fitting therapy to postpone the sickness movement. Medical services space comprises of the most significant subdomain called sickness forecast which needs an effective procedure to anticipate the infection in beginning phases. Information digging gives proficient procedures to early infection forecast. This work utilizes Hybrid characterization procedures which incorporates Support Vector Machines (SVM) and K-Nearest Neighbor (KNN) to assemble the danger forecast model for early expectation of diabetes. This danger model predicts the chance of the patient influenced by diabetes after the age of 40. The dataset comprises of set of retinal pictures of high nearsightedness and Central Serous Retinopathy of PCOS, pancreatitis and way of life issue. This work likewise clarifies the relationship of illnesses like PCOS and pancreatic malignant growth with diabetes. Characterization strategies like Adaboost, Gradient boosting, Decision tree, Navies Bayes, Logistic relapse and neural organization are contrasted with discover a calculation with high exactness.
ISSN:0094-243X
1551-7616
DOI:10.1063/5.0110314