DIABETIC TYPE CLASSIFICATION USING SUPERVISED MACHINE  LEARNING APPROACHES

SARITA KUMARI, DR. AMRITA UPADHAYA ,MONIKA

DIABETIC TYPE CLASSIFICATION USING SUPERVISED MACHINE LEARNING APPROACHES

Authors

SARITA KUMARI, DR. AMRITA UPADHAYA ,MONIKA

Keywords:

Diabetes, SVM, Decision Trees, KNN, ANN, Gradient, Naive Bayes, Random Forest

Abstract

Diabetic Retinopathy (DR) is the leading cause of blindness worldwide and a serious diabeticcomplication. To prevent vision loss, DR lesion diagnosis and categorization must be done early. DRearly detection and treatment can significantly reduce the risk of vision loss. This paper focuses onclassifying a sample into diabetic and non-diabetic using a variety of techniques,

References

Diabetesdetectionusingmachinelearningclassificationmethods. URLehttp://dx.doi.org/10.1109/ICIT52682.2021.9491788

Diabetespredictionusingdifferentmachinelearningclassifiers.URLewww.ijtrd.com

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Published

2024-12-25

How to Cite

SARITA KUMARI, DR. AMRITA UPADHAYA ,MONIKA. (2024). DIABETIC TYPE CLASSIFICATION USING SUPERVISED MACHINE LEARNING APPROACHES. Journal of Computational Analysis and Applications (JoCAAA), 33(08), 4471–4485. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/2856