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IJSTR >> Volume 9 - Issue 6, June 2020 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



Improving Type II Diabetes Prediction using Bayesian Optimization

[Full Text]

 

AUTHOR(S)

Parvathy Dharmarajan, B Rajathilagam

 

KEYWORDS

Neural Network, Levenberg-Marquardt, Bayesian Regularization, Type II diabetes, Error Analysis.

 

ABSTRACT

Nowadays, one of the prominent diseases prevalent around the world is diabetes. Within which there are of two main kinds, namely Type I and Type II. The objective of this study is to evaluate various Neural Networks (NN) based algorithms for the detection of Type II diabetes. Experiments was conducted on different training algorithms for the predictive ability. In the process, fine-tuning was done upon different parameters such as the number of neurons, number of hidden layers, etc. to determine the performance of the neural network. The main focus was to minimize the error rate in the training sample by using the neural network models. This study helps the researchers working on the domain to select the optimal algorithm for the design of models based on Neural networks.

 

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