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- W4366375462 abstract "Nerve pain, eye difficulties, skin issues, renal problems, heart disease, depression, and a host of other illnesses are all linked to diabetes if left untreated. In an effort to lengthen the human lifespan, scientists all around the world have applied a variety of methods for early disease prediction. Diseases of the eyes, heart, and kidneys are prioritised because they can cause serious consequences and even death if left untreated. After conducting an in-depth analysis of the strategies used by several researchers, the following models were constructed and tested using the datasets most frequently favoured by leading scientists throughout the world. Diabetic retinopathy (DR) is an eye condition, and this research seeks to construct a hybrid learning model for early prediction of DR. The proposed approach combines elements of both Machine Learning and Deep Learning. Score Level Fusion is the last arbiter of the model's precision. Images from the first dataset of its sort reflecting the Indian population, the Indian Diabetic Retinopathy Imaging Dataset, were used to make the prediction. Predicting the DR and recommending the necessary treatment to patients is possible based on severity levels, which ultimately leads to the elimination of blindness." @default.
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- W4366375462 date "2023-02-24" @default.
- W4366375462 modified "2023-09-30" @default.
- W4366375462 title "An Effective Expectation of Diabetes Mellitus via Improved Support Vector Machine through Cloud Security" @default.
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- W4366375462 doi "https://doi.org/10.1109/icicacs57338.2023.10099888" @default.
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