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Prediction of Diabetes Disease Using Data Mining and Deep Learning Techniques

EasyChair Preprint no. 1608

10 pagesDate: October 9, 2019


Abstract - Diabetes Mellitus is one of the growing fatal diseases all over the world. It leads to complications that include heart disease, stroke, and nerve disease, kidney damage. So, Medical Professionals want a reliable prediction system to diagnose Diabetes. To predict the diabetes at earlier stage, different machine learning techniques are useful for examining the data from different sources and valuable knowledge is synopsized. So mining the diabetes data in an efficient way is a crucial concern. In this project, a medical dataset has been accomplished to predict the diabetes. The R-Studio and Pypark software was employed as a statistical computing tool for diagnosing diabetes. The PIMA Indian database was acquired from UCI repository will be used for analysis. The dataset was studied and analyzed to build an effective model that predicts and diagnoses the diabetes disease earlier.

Keyphrases: Classification, Clustering, CNN, Data Mining, Decision Tree, Deep Convolutional Neural Network, Diabetes, k-NN, linear regression, logistic regression, machine learning, Naive Baye, neural networks, Random Forest, Regression, RNN, Simple Linear Regression, supervised learning algorithm, SVM

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Tharak Roopesh and Asadi Srinivasulu and K.S. Kannan},
  title = {Prediction of Diabetes Disease Using Data Mining and Deep Learning Techniques},
  howpublished = {EasyChair Preprint no. 1608},

  year = {EasyChair, 2019}}
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