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Chowdary, P.B.K. and Kumar, R.U. (2021) An Effective Approach for Detecting Diabetes Using Deep Learning Techniques Based on Convolutional LSTM Networks. International Journal of Advanced Computer Science and Applications, 12, 519-525.
https://doi.org/10.14569/IJACSA.2021.0120466

has been cited by the following article:

• TITLE: Leveraging Pima Dataset to Diabetes Prediction: Case Study of Deep Neural Network

  AUTHORS: Pélagie Hounguè, Annie Ghylaine Bigirimana

  KEYWORDS: Deep Learning, Artificial Intelligence, Deep Neural Network, k-Fold Cross-Validation, Diabete Mellitus

  JOURNAL NAME: Journal of Computer and Communications, Vol.10 No.11, November 2, 2022

  ABSTRACT: Diabetes is a chronic disease. In 2019, it was the ninth leading cause of death with an estimated 1.5 million deaths. Poorly controlled, diabetes can lead to serious health problems. That explains why early diagnosis of diabetes is very important. Several approaches that use Artificial Intelligence, specifically Deep Learning, have been widely used with promising results. The contribution of this paper is in two-folds: 1) Deep Neural Network (DNN) approach is used on Pima Indian dataset to predict diabetes using 10 k-fold cross validation and 89% accuracy is obtained; 2) comparative analysis of previous work is provided on diabetes prediction using DNN with the tested model. The results showed that 10 k-fold cross-validation could decrease the efficiency of diabetes prediction models using DNN.