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Cloud-Based Diabetic Prediction Framework: Deep Learning Approach

EasyChair Preprint no. 9433

6 pagesDate: December 8, 2022


Since Diabetic is one of most common growing disease in the world. Which open the gate for another kind of diseases such as blindness, kidney problems, heart disease and more. Therefore, we need to develop a system the predict diabetic before it happens to people and advise them to avoid it. The system is more than early detection as its prediction. We propose a cloud-based secure framework that integrates traditional machine learning methods with deep neural networks. The system collects patients readings using IoT devices and sensors, where it will be moved securely using public key encryption to cloud storage. Then the prediction algorithm performs on time prediction on the data to see if the patient expected to be diabetic in the future or not. The prediction techniques tested on Pima Indian diabetic dataset from UCI. The result shows that it performs traditional ML methods with accuracy of 98%.

Keyphrases: deep learning, Deep Neural Network, Diabetic, Diabetic prediction, e-health, IoT, machine learning

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Monther Tarawneh and Fiasal Alzyoud and Yousef Alsharrab and Khalaf Khatatneh},
  title = {Cloud-Based Diabetic Prediction Framework: Deep Learning Approach},
  howpublished = {EasyChair Preprint no. 9433},

  year = {EasyChair, 2022}}
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